U.S. patent application number 16/477311 was filed with the patent office on 2020-01-30 for slam method and apparatus robust to wireless environment change.
This patent application is currently assigned to Korea Institute of Science and Technology. The applicant listed for this patent is Korea Institute of Science and Technology. Invention is credited to Youngmin JHON, Jaehun KIM, Taikjin LEE, Minah SEO, Beomju SHIN.
Application Number | 20200033463 16/477311 |
Document ID | / |
Family ID | 63251649 |
Filed Date | 2020-01-30 |
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United States Patent
Application |
20200033463 |
Kind Code |
A1 |
LEE; Taikjin ; et
al. |
January 30, 2020 |
SLAM METHOD AND APPARATUS ROBUST TO WIRELESS ENVIRONMENT CHANGE
Abstract
The present invention relates to SLAM (simultaneous localization
and mapping) method and apparatus robust to a wireless environment
change. A relative position of a moving node is estimated based on
motion sensing of the moving node, the relative position of the
moving node is corrected based on a comparison between a change
pattern of at least one signal strength received over a plurality
of time points and a signal strength distribution in a region in
which the moving node is located, a route of the region is
represented by using the relative position corrected as described
above, and thereby, it is possible accurately estimate a position
of the moving node and to create a map in which very accurate route
information is recorded throughout the entire region at the same
time, even if a wireless environment change such as signal
interference between communication channels, expansion of an access
point, and occurrence of a failure or an obstacle is made or a poor
wireless environment such as lack of the number of access points
occurs.
Inventors: |
LEE; Taikjin; (Seoul,
KR) ; JHON; Youngmin; (Seoul, KR) ; KIM;
Jaehun; (Seoul, KR) ; SEO; Minah; (Seoul,
KR) ; SHIN; Beomju; (Seoul, KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Korea Institute of Science and Technology |
Seoul |
|
KR |
|
|
Assignee: |
Korea Institute of Science and
Technology
Seoul
KR
|
Family ID: |
63251649 |
Appl. No.: |
16/477311 |
Filed: |
December 28, 2017 |
PCT Filed: |
December 28, 2017 |
PCT NO: |
PCT/KR2017/015651 |
371 Date: |
July 11, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01S 5/0252 20130101;
G01S 11/06 20130101 |
International
Class: |
G01S 11/06 20060101
G01S011/06; G01S 5/02 20060101 G01S005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 25, 2017 |
KR |
10-2017-0011988 |
Sep 26, 2017 |
KR |
10-2017-0124468 |
Nov 21, 2017 |
KR |
10-2017-0155888 |
Claims
1. A SLAM (simultaneous localization and mapping) method of a
moving node comprising: estimating a relative position of the
moving node, based on motion sensing of the moving node; generating
a change pattern of at least one signal strength that is received
over a plurality of time points; correcting the estimated relative
position, based on a comparison between the generated change
pattern of the signal strength and a signal strength distribution
in a region in which the moving node is located; and creating a map
for the region by representing a route of the region using the
corrected relative position.
2. The SLAM method of claim 1, wherein the change pattern of the at
least one signal strength is a change pattern of at least one
signal strength that is represented as continuous arrangement of at
least one signal strength which is received a plurality of times at
a plurality of relative positions of the moving node that are
estimated at the plurality of time points.
3. The SLAM method of claim 1, further comprising: estimating a
reception position of a moving node for at least one signal that is
received at a current time point among the plurality of time points
based on a comparison between the generated change pattern of the
signal strength and the signal strength distribution, wherein the
correcting of the relative position corrects the estimated relative
position by correcting a coordinate value of the estimated relative
position using a coordinate value of the estimated reception
position.
4. The SLAM method of claim 3, further comprising: searching a part
having a pattern most similar to the change pattern of the
generated signal strength within the signal strength distribution
by comparing the generated change pattern of the signal strength
with the signal strength distribution, wherein the estimating of
the reception position estimates an absolute position in the region
that is indicated by the searched part as a reception position of
the moving node.
5. The SLAM method of claim 3, further comprising: searching,
within the signal strength distribution, a surface part having a
shape most similar to a pattern of a geometric surface shape that
graphically representing a change of at least one signal strength
according to a relative change of a position of the moving node,
wherein the estimating of the reception position estimates an
absolute position in the region that is indicated by the searched
surface part as a reception position of the moving node.
6. The SLAM method of claim 5, wherein the generating of the change
pattern of the at least one signal strength generates the pattern
of the geometric surface shape in such a manner that a dot is
marked on a point of multidimensional space that is determined by
mapping an ID of a certain fixed node on a first coordinate axis of
the multidimensional space, mapping the relative position of the
moving node on a second coordinate axis, and mapping strength of a
signal that is transmitted from the certain fixed node on a third
coordinate axis.
7. The SLAM method of claim 3, wherein the correcting of the
relative position corrects the coordinate value of the estimated
relative position in such a manner that a difference between the
coordinate value of the estimated reception position and a
coordinate value of a localization point closest to the coordinate
value of the reception position among localization points within
the signal strength distribution is minimized, and wherein the
localization points are points where the relative position of the
moving node is measured on a movement route of the moving node.
8. The SLAM method of claim 7, wherein the correcting of the
relative position minimizes an error between the coordinate value
of the estimated relative position and a coordinate value of
another relative position of the moving node based on the estimated
relative position, and simultaneously corrects the coordinate value
of the estimated relative position in such a manner that the
difference between the coordinate value of the estimated reception
position and the coordinate value of the localization point closest
to the coordinate value of the reception position among the
localization points within the signal strength distribution is
minimized.
9. The SLAM method of claim 7, wherein the correcting of the
relative position corrects the coordinate value of the estimated
relative position in such a manner that a difference between a
coordinate value of an arrival point which is the estimated
reception position and a coordinate value of a starting point which
is a localization point closest to the coordinate value of the
arrival point among the localization points within the signal
strength distribution is minimized, and wherein the moving node
starts from the starting point, turns a route of a route form, and
arrives at the arrival point corresponding to a geographically
identical position to the starting point.
10. The SLAM method of claim 3, wherein the estimating of the
reception position includes, measuring strength of at least one
signal that is transmitted from the at least one fixed node;
generating a change pattern of at least one signal strength
according to a relative change of a position of a moving node over
a plurality of time points from the measured at least one signal
strength and the relative position of the estimated moving node;
and estimating the reception position, based on a comparison
between the change pattern of the generated at least one signal
strength and distribution of signal strength in the region.
11. The SLAM method of claim 10, wherein the generating of the
change pattern of the at least one signal strength generates the
change pattern of the at least one signal strength by accumulating
pattern data representing a pattern of at least one signal strength
that is received from the at least one fixed node at the estimated
relative position, on pattern data with respect to a relative
position which is estimated before the relative position is
estimated.
12. The SLAM method of claim 11, wherein the generating of the
change pattern of the at least one signal strength generates the
pattern data from spatial domain data representing the measured
each signal strength in association with the estimated relative
position.
13. The SLAM method of claim 1, wherein the measuring of the signal
strength, the estimating of the relative position, the generating
of the pattern, and the correcting of the relative position are
repeatedly performed for each of the plurality of time points, and
wherein the creating of the map represents the route of the region
by arranging and recording coordinate values of a plurality of
relative positions that are corrected at the plurality of time
points, and generates the map by mapping to the coordinate value of
the relative position that are corrected at the each time point and
recording strength of at least one signal that are received at the
same point of the each time point.
14. A computer-readable recording medium comprising: a program for
causing a computer to perform the method of claim 1.
15. A SLAM (simultaneous localization and mapping) apparatus of a
moving node comprising: a relative localization unit that estimates
a relative position of the moving node, based on motion sensing of
the moving node; a wireless localization unit that estimates a
reception position of the moving node for a signal that is received
at a current time point among a plurality of time points, based on
a change pattern of at least one signal strength which is received
over the plurality of time points; a position correction unit that
correcting the estimated relative position by correcting a
coordinate value of the estimated relative position using a
coordinate value of the estimated reception position; and a map
creation unit that creates a map for a region by representing a
route of the region in which the moving node is located by using
the corrected relative position.
16. The SLAM apparatus of claim 15, wherein the wireless
localization unit includes, a signal processing unit that measures
strength of at least one signal which is transmitted from the at
least one fixed node; a pattern generation unit that generates a
change pattern of at least one signal strength according to a
relative change of a position of a moving node over the plurality
of time points from the measured at least one signal strength and
the relative position of the estimated moving node; and a reception
position estimation unit that estimates the reception position of
the moving node, based on a comparison between the change pattern
of the generated at least one signal strength and signal strength
distribution in the region.
17. The SLAM apparatus of claim 16, further comprising: a buffer
that accumulates pattern data which is generated by the pattern
generation unit, wherein the pattern generation unit generates the
change pattern of the at least one signal strength by accumulating
pattern data representing a pattern of at least one signal strength
that is received from the at least one fixed node at the estimated
relative position on pattern data which is stored in the buffer and
storing the accumulated data.
18. The SLAM apparatus of claim 15, further comprising: a storage
that stores a map representing distribution of signal strength in
the region, wherein the wireless localization unit estimates the
reception position, based on a comparison between the change
pattern of the signal strength and the signal strength distribution
of the map stored in the storage.
Description
TECHNICAL FIELD
[0001] The present invention relates to SLAM method and apparatus
which can estimate a position of a moving node in an unknown
environment and simultaneously create a map for the unknown
environment.
BACKGROUND ART
[0002] Recently, interest in a simultaneous localization and
mapping (SLAM) technology is increasing in a field of moving robot
such as a drone. SLAM is a technology for creating a map for the
unknown environment while a robot walks around and recognizes a
position thereof using only a sensor attached to the robot without
external help, and emerges as a key technology for an autonomous
navigation. SLAM of related art estimates a position of a moving
robot on the basis of several physical landmarks. In order to
identify the landmarks, a sensor such as a LiDAR, a camera, or an
ultrasonic sensor is required.
[0003] The LiDAR has a very high resolution but is expensive and
hard to be applied to a small and light device such as a smartphone
due to a limitation in miniaturization. In the same manner, the
camera is hard to be applied to the smartphone having a low image
data processing capability because the camera outputs image data.
The ultrasonic sensor can be miniaturized, but has a very low
resolution, and thereby, there is a limitation to create a map with
high accuracy. Due to this, the SLAM is attracting attention for a
special purpose such as a moving robot put in a disaster region but
is not attracting attention for creating a map for localization of
a navigation system of a smartphone or a vehicle except for the
moving robot.
[0004] A global positioning system (GPS)-based map and a wireless
localization map are representative examples of a map that is
commercialized or researched for localization of a navigation
system of a smartphone or a vehicle. The map of related art has a
problem that takes a lot of time and cost to create a map because a
person creates the map by collecting terrain information or signal
information while moving around a region in which a localization
service is provided. Particularly, the GPS cannot perform the
localization in an indoor space where a radio wave emitted from a
satellite cannot reach, and there is a problem that accuracy of the
localization in a city is seriously decreased due to blocking,
reflection or the like of the radio wave by a high-rise
building.
[0005] Recently, automobile manufacturers around the world, and
global corporations such as Google and Intel have fostered research
and development of an autonomous vehicle. However, partial
autonomous driving in an outdoor space makes some results, but
autonomous driving in the indoor space and the outdoor space is
still impossible due to inability of an indoor localization of the
GPS. In order to solve the problem of the GPS, a wireless
localization technique for estimating a position of a user or a
vehicle using a radio signal existing in an indoor space draws much
attention. The wireless localization technology is currently being
commercialized and serviced, but localization accuracy is very low
compared with the GPS, and thus, various types of wireless
localization technology are under development.
[0006] Wireless communication can be classified into short-range
wireless communication and wide-area wireless communication. A
representative example of the short-range wireless communication
includes Wi-Fi, Bluetooth, Zigbee, and the like, and a
representative example of the wide-area wireless communication
includes 3rd generation (3G), 4th generation (4G), Lora, and the
like. The long term evolution (LTE) is a kind of 4G wireless
communication. The short-range wireless communication such as
Bluetooth and ZigBee is not suitable for a localization because of
characteristics that temporarily occur in an indoor space according
to needs of a user and disappear. Currently, a Wi-Fi signal and an
LTE signal are known to be distributed in most indoor spaces.
[0007] Accordingly, a WiFi position system (WPS) that performs a
localization using a Wi-Fi signal of a band of 2.4 GHz is in the
spotlight. A representative localization technique which uses the
WiFi signal may include a fingerprint technique.
[0008] This technique divides the indoor space into a grid
structure, collects values of signal strength in each unit area,
and builds a radio map by storing the values in a database. In a
state where the radio map is built as described above, a position
of a user is estimated by comparing strength of the signal received
at the position of the user with data of the radio map. Since the
technique collects data in which spatial characteristics of the
indoor space is reflected, the technique has an advantage that
localization accuracy is higher than the triangulation technique.
As wireless environment is good and many signals are collected by
finely dividing the indoor space, the localization precision may be
increased up to 2 to 3 meters.
[0009] The fingerprint technique performs relatively accurate
localization in a case where there is little difference between
strength of a signal collected at the time of building a radio map
and strength of a signal collected at the time of localization.
However, a change in the wireless environment, such as a signal
interference between communication channels frequently occurring in
the real world, expansion of an access point, occurrence of failure
or an obstacle, and the like leads to collection of signal strength
different from data of a radio map built in the past, which results
in a serious impact on localization accuracy. Accordingly, various
attempts have been made to increase the localization accuracy by
applying a k-nearest neighbor (KNN), a particle filter or the like
to the fingerprint technique.
[0010] First of all, due to the fact that a Wi-Fi signal is
distributed actually only in a part of the center of a city due to
characteristic of short-range wireless communication, the
fingerprint technique has an inherent limitation that cannot be
used alone for a vehicle navigation system requiring a localization
service in both an indoor space and an outdoor space, or autonomous
driving. The LTE signal is uniformly distributed in the indoor
space and the outdoor space, but there is a limitation to increase
a localization accuracy because an area where a change in the
signal strength is not large is wide. In this way, a GPS-based map
cannot support an indoor space, and a map for wireless localization
has a problem in which not only there is a limitation in increasing
localization accuracy thereof, but also lots of time and cost are
consumed to create the map because a person directly collects
terrain information or signal information of a region in which a
localization service is provided.
DISCLOSURE
Technical Problem
[0011] There is provided SLAM method and apparatus robust to
wireless environment change which can accurately estimate a
position of a moving node and can create a map with very accurate
route information throughout the entire region at the same time,
even if the wireless environment change such as signal interference
between communication channels, expansion of an access point, and
occurrence of a failure or an obstacle is made or a poor wireless
environment such as lack of the number of access points occurs and
can accurately estimate the position of the moving node without a
separate sensor for identifying a physical landmark and create a
map with very accurate route information throughout the entire
region at the same time. In addition, there is provided a
computer-readable recording medium in which a program for causing a
computer to execute the above-described SLAM method is recorded.
The present invention is not limited to the above-described
technical problems as described above, and another technical
problem may be derived from the following description.
Technical Solution
[0012] A SLAM (simultaneous localization and mapping) method of a
moving node according to one aspect of the present invention
includes estimating a relative position of the moving node, based
on motion sensing of the moving node; generating a change pattern
of at least one signal strength that is received over a plurality
of time points; correcting the estimated relative position, based
on a comparison between the generated change pattern of the signal
strength and a signal strength distribution in a region in which
the moving node is located; and creating a map for the region by
representing a route of the region using the corrected relative
position.
[0013] The change pattern of the at least one signal strength may
be a change pattern of at least one signal strength that is
represented as continuous arrangement of at least one signal
strength which is received a plurality of times at a plurality of
relative positions of the moving node that are estimated at the
plurality of time points. The SLAM method may further includes
estimating a reception position of a moving node for at least one
signal that is received at a current time point among the plurality
of time points based on a comparison between the generated change
pattern of the signal strength and the signal strength
distribution, and the correcting of the relative position may
correct the estimated relative position by correcting a coordinate
value of the estimated relative position using a coordinate value
of the estimated reception position.
[0014] The SLAM method may further include searching a part having
a pattern most similar to the change pattern of the generated
signal strength within the signal strength distribution by
comparing the generated change pattern of the signal strength with
the signal strength distribution, and the estimating of the
reception position may estimate an absolute position in the region
that is indicated by the searched part as a reception position of
the moving node.
[0015] The SLAM method may further include searching, within the
signal strength distribution, a surface part having a shape most
similar to a pattern of a geometric surface shape that graphically
representing a change of at least one signal strength according to
a relative change of a position of the moving node, and the
estimating of the reception position may estimate an absolute
position in the region that is indicated by the searched surface
part as a reception position of the moving node.
[0016] The generating of the change pattern of the at least one
signal strength may generate the pattern of the geometric surface
shape in such a manner that a dot is marked on a point of
multidimensional space that is determined by mapping an ID of a
certain fixed node on a first coordinate axis of the
multidimensional space, mapping the relative position of the moving
node on a second coordinate axis, and mapping strength of a signal
that is transmitted from the certain fixed node on a third
coordinate axis.
[0017] The correcting of the relative position may correct the
coordinate value of the estimated relative position in such a
manner that a difference between the coordinate value of the
estimated reception position and a coordinate value of a
localization point closest to the coordinate value of the reception
position among localization points within the signal strength
distribution is minimized, and the localization points may be
points where the relative position of the moving node is measured
on a movement route of the moving node.
[0018] The correcting of the relative position may minimize an
error between the coordinate value of the estimated relative
position and a coordinate value of another relative position of the
moving node based on the estimated relative position, and may
simultaneously correct the coordinate value of the estimated
relative position in such a manner that the difference between the
coordinate value of the estimated reception position and the
coordinate value of the localization point closest to the
coordinate value of the reception position among the localization
points within the signal strength distribution is minimized.
[0019] The correcting of the relative position may correct the
coordinate value of the estimated relative position in such a
manner that a difference between a coordinate value of an arrival
point which is the estimated reception position and a coordinate
value of a starting point which is a localization point closest to
the coordinate value of the arrival point among the localization
points within the signal strength distribution is minimized, and
the moving node may start from the starting point, turn a route of
a route form, and arrive at the arrival point corresponding to a
geographically identical position to the starting point.
[0020] The estimating of the reception position may include
measuring strength of at least one signal that is transmitted from
the at least one fixed node; generating a change pattern of at
least one signal strength according to a relative change of a
position of a moving node over a plurality of time points from the
measured at least one signal strength and the relative position of
the estimated moving node; and estimating the reception position,
based on a comparison between the change pattern of the generated
at least one signal strength and distribution of signal strength in
the region.
[0021] The generating of the change pattern of the at least one
signal strength may generate the change pattern of the at least one
signal strength by accumulating pattern data representing a pattern
of at least one signal strength that is received from the at least
one fixed node at the estimated relative position, on pattern data
with respect to a relative position which is estimated before the
relative position is estimated. The generating of the change
pattern of the at least one signal strength may generate the
pattern data from spatial domain data representing the measured
each signal strength in association with the estimated relative
position.
[0022] The measuring of the signal strength, the estimating of the
relative position, the generating of the pattern, and the
correcting of the relative position may be repeatedly performed for
each of the plurality of time points, and the creating of the map
may represent the route of the region by arranging and recording
coordinate values of a plurality of relative positions that are
corrected at the plurality of time points, and may generate the map
by mapping to the coordinate values of the relative positions that
are corrected at the each time point and recording strength of at
least one signal that are received at the same point of the each
time point.
[0023] According to another aspect of the present invention, there
is provided a computer-readable recording medium including a
program for causing a computer to perform the SLAM method.
[0024] According to still another aspect of the present invention,
there is provided a SLAM (simultaneous localization and mapping)
apparatus of a moving node including a relative localization unit
that estimates a relative position of the moving node, based on
motion sensing of the moving node; a wireless localization unit
that estimates a reception position of the moving node for a signal
that is received at a current time point among a plurality of time
points, based on a change pattern of at least one signal strength
which is received over the plurality of time points; a position
correction unit that correcting the estimated relative position by
correcting a coordinate value of the estimated relative position
using a coordinate value of the estimated reception position; and a
map creation unit that creates a map for a region by representing a
route of the region in which the moving node is located by using
the corrected relative position.
[0025] The wireless localization unit may include a signal
processing unit that measures strength of at least one signal which
is transmitted from the at least one fixed node; a pattern
generation unit that generates a change pattern of at least one
signal strength according to a relative change of a position of a
moving node over the plurality of time points from the measured at
least one signal strength and the relative position of the
estimated moving node; and a reception position estimation unit
that estimates the reception position of the moving node, based on
a comparison between the change pattern of the generated at least
one signal strength and signal strength distribution in the
region.
[0026] The SLAM apparatus may further include a buffer that
accumulates pattern data which is generated by the pattern
generation unit, and pattern generation unit may generate the
change pattern of the at least one signal strength by accumulating
pattern data representing a pattern of at least one signal strength
that is received from the at least one fixed node at the estimated
relative position on pattern data which is stored in the buffer and
storing the accumulated data.
[0027] The SLAM apparatus may further include a storage that stores
a map representing distribution of signal strength in the region,
the wireless localization unit may estimate the reception position,
based on a comparison between the change pattern of the signal
strength and the signal strength distribution of the map stored in
the storage.
Advantageous Effects
[0028] a relative position of a moving node is estimated based on
motion sensing of the moving node, the relative position of the
moving node is corrected based on a comparison between a change
pattern of at least one signal strength received over a plurality
of time points and a signal strength distribution in a region in
which the moving node is located, a route of the region is
represented by using the relative position corrected as described
above, and thereby, it is possible accurately estimate a position
of the moving node and to create a map in which very accurate route
information is recorded throughout the entire region at the same
time, even if a wireless environment change such as signal
interference between communication channels, expansion of an access
point, and occurrence of a failure or an obstacle is made or a poor
wireless environment such as lack of the number of access points
occurs. In this way, since position estimation of the moving node
and map creation can be performed at the same time, time and cost
consumed in the map creation can be greatly reduced compared with a
method in which a person directly collects terrain information or
signal information.
[0029] A reception position of the moving node is estimated for at
least one signal received at a current time point among a plurality
of time points, based on a comparison between a change pattern of
at least one signal strength received over the plurality of time
points and signal strength distribution in a region where the
moving node is located, a coordinate value of a relative position
of the moving node is corrected by using a coordinate value of the
reception position estimated in this way, and thereby, it is
possible to accurately estimate a position of the moving node and
to simultaneously create a map in which very accurate route
information is recorded throughout the entire region by performing
mutual complementing between defects in the relative localization
and defects in the wireless localization even in various
environment changes such as a wireless environment change and a
route change.
[0030] Unlike the SLAM of related art, a physical landmark is not
required, and instead, similarity between a change pattern of at
least one signal strength received over a plurality of time points
and a corresponding pattern in signal strength distribution in a
region in which the moving node is located serves as a kind of
landmark, and thus, it is possible to estimate a position of the
moving node and simultaneously create a map, based on pattern
similarity which is much lower in complexity than image processing
of the SLAM of related art. As a result, a SLAM algorithm can be
executed smoothly without a separate sensor for identifying a
physical landmark even in a small and light moving node such as a
smartphone.
[0031] Even in a case where the position of the moving node is
estimated by using a radio signal having almost no change in signal
strength over a wide area, such as an LTE signal, a position of the
moving node can be accurately estimated by using the change pattern
of at least one signal strength received over a plurality of time
points. This is because, even if there is almost no change in the
signal strength between the adjacent localization points on a
movement route of the moving node, strength of the LTE signal
sufficiently changes to the extent that the position of the moving
node is accurately estimated within a movement distance
corresponding to a length of a change pattern of the signal
strength used for the wireless localization of the present
invention.
[0032] Since the position of the moving node can be accurately
estimated by using the LTE signal in which the signal strength
rarely changes between the measurement points on the movement
route, it is possible to create a map that can cover an indoor
space and an outdoor space. As a result, it is possible to provide
a map in which indoor and outdoor localization with high accuracy
can be made in an urban area without influence of a high-rise
building by using LTE signals widely distributed in the building
and in an inner city, and thereby, it is possible to replace a
GPS-based map which is most widely used in a vehicle navigation
system nowadays but cannot be used for indoor localization and has
seriously degraded localization accuracy in the center of a city,
and to accelerate realization of fully autonomous travel in outdoor
and indoor spaces.
DESCRIPTION OF THE DRAWINGS
[0033] FIG. 1 is a configuration diagram of a wireless
communication system according to an embodiment of the present
invention.
[0034] FIG. 2 is a configuration diagram of a SLAM apparatus of a
moving node illustrated in FIG. 1.
[0035] FIG. 3 is a flowchart of a SLAM method according to an
embodiment of the present invention.
[0036] FIG. 4 is a detailed flowchart of step 130 illustrated in
FIG. 3.
[0037] FIG. 5 is a diagram illustrating a pattern formation
principle in step 430 of FIG. 4.
[0038] FIG. 6 is a diagram illustrating a three-dimensional spatial
coordinate system for generating a change pattern of a signal
strength used for a SLAM algorithm according to the present
embodiment.
[0039] FIGS. 7A and 7B are table forms illustrating accumulation of
pattern data used for SLAM according to the present embodiment.
[0040] FIG. 8 is a diagram illustrating an example of a target
region of map creation according to the SLAM of the present
embodiment.
[0041] FIG. 9 is a diagram illustrating an example in which a
change pattern of signal strength used for the SLAM according to
the present embodiment is generated.
[0042] FIGS. 10A to 11B are diagrams illustrating examples in which
a received position of the moving node is estimated in accordance
with the SLAM algorithm according to the present embodiment.
[0043] FIGS. 11A and 11B are diagrams illustrating an example in
which accuracy of an absolute position estimated by the wireless
localization algorithm according to the present embodiment is
lowered.
[0044] FIGS. 12A to 12C are movement trajectory diagrams of the
moving node estimated by a PDR algorithm in the target region
illustrated in FIG. 8.
[0045] FIGS. 13A to 13C are diagrams illustrating an example of
correction of a relative position made by the SLAM according to the
present embodiment.
[0046] FIGS. 14A to 15F are diagrams illustrating a comparison
between a SLAM performance test for a wireless localization
algorithm of related art and a SLAM performance test for the
wireless localization algorithm according to the present
embodiment.
[0047] FIGS. 16A and 16B are diagrams illustrating examples of maps
created by the SLAM algorithm according to the present
embodiment.
MODE OF THE INVENTION
[0048] Hereinafter, embodiments of the present invention will be
described in detail with reference to the drawings. Hereinafter,
all moving objects, which are localization targets, such as a
smartphone carried by a user, a navigation system mounted on a
vehicle, and a moving robot that moves by itself as an independent
object will be collectively referred to as a moving node. In
addition, communication devices, which are fixedly installed in
regions and relay wireless communication of a moving node, such as
an access point (AP) of a WiFi network and a base station of an LTE
network, will be collectively referred to as a "fixed node". In
addition, a radio frequency (RF) signal transmitted from the fixed
node will be briefly referred to as a "signal".
[0049] An embodiment of the present invention that will be
described below relates to simultaneous localization and mapping
(SLAM) method and apparatus which can estimate a position of a
moving node in an unknown environment and create a map for the
unknown environment at the same time, and particularly, to SLAM
method and apparatus robust to a wireless environment change which
can accurately estimate a position of a moving node and can create
a map with very accurate route information throughout the entire
region at the same time, even if a wireless environment change such
as signal interference between communication channels, expansion of
an access point, and occurrence of a failure or an obstacle is made
or a poor wireless environment such as lack of the number of access
points occurs, and can accurately estimate the position of the
moving node without a separate sensor for identifying a physical
landmark and create a map with very accurate route information
throughout the entire region at the same time. Hereinafter, the
SLAM method and the SLAM apparatus will be briefly referred to as a
"SLAM method" and a "SLAM apparatus".
[0050] FIG. 1 is a configuration diagram of a wireless
communication system according to an embodiment of the present
invention. Referring to FIG. 1, the wireless communication system
according to the present embodiment is configured with a plurality
of moving nodes 1, a plurality of fixed nodes 2, and a localization
server 3. Each of the plurality of moving nodes 1 performs wireless
communication with another node through at least one type of
wireless communication network while moving in a state of being
carried by a user or mounted on a vehicle or moving by itself as an
independent object. In general, each moving node 1 performs
wireless communication through at least two types of wireless
communication networks, for example, a Wi-Fi network and an LTE
network. Each of the plurality of fixed nodes 2 relays the wireless
communication of each moving node 1 such that each moving node 1
can access the wireless communication network to perform wireless
communication with nodes. In a case where the moving node 1
performs wireless communication through the Wi-Fi network, the
fixed node may be an access point, and in a case where the moving
node performs the wireless communication through an LTE network,
the fixed node may be a base station.
[0051] The localization server 3 stores a map provided from at
least one of the plurality of moving nodes 1. Any one of the
plurality of moving nodes 1 can create a map of the entire region
where the wireless localization service will be provided and
provide the map to the localization server 3. The region where the
wireless localization service will be provided can be divided and
be assigned to each of the plurality of moving nodes 1. Each moving
node 1 can create a map of the regions which will be assigned to
each moving node 1 and provide the map to the localization server
3. The localization server 3 can complete the map of the entire
region where the wireless localization service will be provided by
combining the maps provided from the plurality of moving nodes 1.
The localization server 3 provides the stored map to the moving
node which will perform wireless localization. As described below,
the map stored in the localization server 3 includes many reference
points and a kind of radio map in which signal strengths at each
reference point are recorded, the map can also be used for other
general wireless localization, in addition to the wireless
localization based on a surface correlation according to the
present embodiment.
[0052] FIG. 2 is a configuration diagram of the SLAM apparatus of
the moving node 1 illustrated in FIG. 1. Referring to FIG. 2, the
SLAM apparatus of the moving node 1 illustrated in FIG. 1 includes
a wireless communication unit 10, a sensor unit 20, a storage 30, a
buffer 40, a relative localization unit 50, a wireless localization
unit 60, a position correction unit 70, and a map creation unit 80.
Those skilled in the art will appreciate that such configuration
elements may be realized by hardware which provides a particular
function or may be realized by a combination of a memory, a
processor, a bus, and the like in which software providing a
particular function is stored. Each of the above-described
configuration elements is not necessarily realized by separate
hardware, and a plurality of the configuration elements may be
realized by common hardware, for example, a combination of a
processor, a memory, a bus, and the like.
[0053] As described above, the moving node 1 may be a smartphone
carried by a user, may be a navigation system mounted on a vehicle,
or may be a moving robot which moves by itself as an independent
object. The embodiment illustrated in FIG. 2 relates to a SLAM
apparatus, and if other configurations of a smartphone, other
configurations of a navigation system, or other configurations of a
moving robot are illustrated in FIG. 2, in addition to the
configuration of the SLAM apparatus illustrated in FIG. 2,
characteristics of the present embodiment may be degraded, and
thus, the other configurations are not illustrated. Those skilled
in the art will understand that, in a case where the moving node 1
is realized by the smartphone, the navigation system, or the moving
robot, other configuration elements besides the configuration
elements illustrated in FIG. 2 can be added.
[0054] The wireless communication unit 10 transmits and receives
signals through at least one wireless communication network. The
sensor unit 20 includes at least one sensor which senses movement
of the moving node 1. The sensor unit 20 may include an
acceleration sensor that measures an acceleration of the moving
node 1 and a gyro sensor that measures an angular velocity of the
moving node 1. A sensor type of the sensor unit 20 may be changed
depending on what type of device the moving node 1 is configured.
In a case where the moving node 1 is configured by a smartphone,
the sensor unit 20 may be configured by an acceleration sensor and
a gyro sensor described above. In a case where the moving node 1 is
configured by a navigation system mounted on a vehicle, the sensor
unit 20 may be configured by the acceleration sensor and the gyro
sensor described above, and an encoder, a geomagnetic sensor, and
the like may be used instead of the sensors.
[0055] The storage 30 stores a map representing a signal strength
distribution in a region where the moving node 1 is located. Here,
the region where the moving node 1 is located indicates a region
(hereinafter, simply referred to as "target region") that becomes a
target of map creation and may be a small region such as an indoor
space of a certain building or may be a large region such as the
center of a city. In a case where the moving node 1 is going to
create a map for a certain region, the moving node 1 collects
strengths of signals transmitted from all the fixed nodes 2 in the
region while travelling through all the routes in the region and
can store a distribution map of the collected signal strengths in
the storage 30. The signal strength distribution map stored in the
storage 30 through a process in which the SLAM method illustrated
in FIG. 3 is repeatedly performed is updated by the number of
travels of the entire route, while the moving node 1 travels all
the routes in the target region several times. In this way, the
signal strength distribution map stored in the storage 30 is
provided to the localization server 3.
[0056] Each time the moving node 1 travels the entire route in the
target region once, the map stored in the storage 30 is updated,
and accuracy of the map stored in the storage 30 is gradually
increased by updating the map. The accuracy of the map stored in
the storage 30 is increased as the number of travels of the entire
route is increased, but the map takes a long time to complete, and
thus, it is preferable that the number of travels of the entire
route is determined by considering the accuracy and required time
of the map stored in the storage 30. The signal intensity
distribution map may be stored in the storage 30 by being
represented in a form of a table in which numerical values of the
respective signal strengths are grouped and may be stored in the
storage 30 by being represented in a form of a graph pattern in
which dots or bars representing respective signal strengths are
connected.
[0057] It is preferable that the map is stored in the storage 30 in
a form of an electronic table so as to provide a map in a form of a
general wireless map that can be used for other general wireless
localization in addition to the wireless localization based on the
surface correlation in the present embodiment. Hereinafter, a point
at which a relative position of the moving node 1 is measured on a
movement route of the moving node 1 moving around the target region
for the purpose of creating the map is referred to simply as a
"localization point". The localization point may be referred to as
a reference point in a field of a radio map of a fingerprint and
may be referred to as a node in a SLAM field of a moving robot. The
buffer 40 is used for accumulating pattern data generated by the
pattern generation unit 15.
[0058] The relative localization unit 50 estimates a relative
position of the moving node 1 on the basis of motion sensing of the
moving node 1 made by the sensor unit 20. The relative localization
unit 50 can estimate the relative position of the moving node 1
using a pedestrian dead reckoning (PDR) algorithm or a dead
reckoning (DR) algorithm widely known in the art to which the
present embodiment belongs. The wireless localization unit 60
estimates a received position of the moving node 1 for a signal
received at a current time point among a plurality of time points
on the basis of a change pattern of at least one signal strength
received from over a plurality of time points which are the current
time point and at least one past time point. Here, the relative
position of the moving node 1 means a relative position with
respect to other positions of the moving node 1, and the reception
position of the moving node 1 means a current position of the
moving node 1 that receives at least one signal transmitted from at
least one fixed node 2 around the moving node 1. Referring to FIG.
2, the wireless localization unit 60 is configured with a scan unit
61, a signal processing unit 62, a domain conversion unit 63, a
pattern generation unit 64, a comparison unit 65, and a reception
position estimation unit 66.
[0059] FIG. 3 is a flowchart of a SLAM method according to an
embodiment of the present invention. Referring to FIG. 3, the SLAM
method according to the present embodiment is configured by the
following steps performed by the SLAM apparatus of the moving node
1 illustrated in FIG. 2. Hereinafter, the relative localization
unit 50 and the wireless localization unit 60 which are illustrated
in FIG. 2 will be described in detail with reference to FIG. 3. In
step 110, the scan unit 61 of the wireless localization unit 60 of
the moving node 1 periodically scans a frequency band of the
wireless communication through the wireless communication unit 10,
thereby, receiving at least one signal transmitted from at least
one fixed node 2. A sampling rate of time domain data which will be
described below is determined according to a length of a scan
period of the scan unit 61. The shorter the scan period of the
wireless communication unit 10, the higher the sampling rate of the
time domain data which will be described below, and as a result,
precision of a reception position of the moving node 1 estimated
according to the present embodiment can be improved.
[0060] Since an ID of the fixed node 2 is included in a signal
transmitted from a certain fixed node 2, it is possible to know the
ID of the fixed node 2 from the signal transmitted from the fixed
node 2. In a case where only one fixed node 2 exists within a
communicable range at a current position of the moving node 1, the
wireless communication unit 10 receives one signal from one fixed
node 2 through a scanning process. In a case where a plurality of
fixed nodes 2 exist within the communicable range at the current
position of the moving node 1, the wireless communication unit 10
receives a plurality of signals corresponding to the plurality of
fixed nodes 2 from the plurality of fixed nodes 2 through the
scanning process. FIG. 1 illustrates an example in which the moving
node 1 receives three signals from three fixed nodes 21, 22, and
23. It can be seen that the other fixed node 24 is located outside
the communicable range of the moving node 1. Since the present
embodiment can be applied to a region where a wireless
communication infrastructure is relatively well equipped, the
moving node 1 mostly receives signals of the plurality of fixed
nodes 2, but a signal of one fixed node 2 can also be received at
some regions where the wireless communication infrastructure is
weak. Meanwhile, in a case where no signal is received in the
scanning process, the localization itself required for the SLAM
according to the present embodiment is impossible, and thereby, the
moving node 1 waits until receiving the signal of the fixed node
2.
[0061] In step 120, the signal processing unit 62 of the wireless
localization unit 60 of the moving node 1 measures strength of each
signal received in step 110. In step 130, the wireless localization
unit 60 of the moving node 1 estimates the reception position of
the moving node 1 for a signal received from at least one fixed
node 2 at a current time point on the basis of a change pattern of
at least one signal strength received from at least one fixed node
2 around the moving node 1 over a plurality of time points. Here,
the change pattern of the at least one signal strength received
from at least one fixed node 2 over the plurality of time points is
a change pattern of at least one signal strength according to a
relative change of a position of the moving node 1 over a plurality
of time points.
[0062] In step 210, the relative localization unit 50 of the moving
node 1 periodically receives an output signal of the sensor unit
20. In step 220, the relative localization unit 50 of the moving
node 1 calculates a movement distance and a movement direction of
the moving node 1 from a value of the output signal of the sensor
unit 20 received in step 210. In step 230, the relative
localization unit 50 of the moving node 1 calculates a relative
change of a current position of the moving node 1 with respect to a
previous position of the moving node 1 on the basis of the movement
distance and the movement direction of the moving node 1 calculated
in step 220, thereby, estimating the current relative position of
the moving node 1 with respect to the previous position of the
moving node 1. As described above, since a sensor type of the
sensor unit 20 can be changed depending on what type of device the
moving node 1 is configured, different navigation algorithms can be
used for estimating the relative position of the moving node 1
depending on what type of device the moving node 1 is
configured.
[0063] For example, in a case where the moving node 1 is a
smartphone, the relative localization unit 50 may estimate the
relative position of the moving node 1 using a PDR algorithm. More
specifically, the relative localization unit 50 can calculate a
movement distance of the moving node 1 by integrating a value of an
output signal of an acceleration sensor of the sensor unit 20, and
can calculate a movement direction in the moving node 1 by
integrating a value of an output signal of a gyro sensor in the
moving node 1. In a case where the moving node 1 is mounted on a
vehicle as a navigation system, the relative localization unit 50
can estimate the relative position of the moving node 1 using a DR
algorithm. For example, the relative localization unit 50 can
calculate the movement distance and the movement direction of the
moving node 1 by attaching the acceleration sensor and the gyro
sensor of the sensor unit 20 to a wheel of a vehicle. In this way,
since the PDR and DR algorithms for estimating the relative
position of the moving node 1 estimate the relative position of the
moving node 1 through integration of the values of the output
signals of the sensors, as estimation of the relative position is
repeated, errors of the relative position of the moving node 1 are
accumulated.
[0064] FIG. 4 is a detailed flowchart of step 130 illustrated in
FIG. 3. Referring to FIG. 3. in step 410, the signal processing
unit 62 of the wireless localization unit 60 of the moving node 1
generates time domain data representing each signal strength
measured in step 120 in association with any one time point. Here,
any one time point is used as information for distinguishing the
signal received in step 110 from the signal received previously or
the signal received thereafter. This time point may be reception
time point of each signal. The reception time point of each signal
may be a time point when the signal processing unit 62 reads time
of an internal timepiece of the moving node 1 at the moment when
each signal is input from the wireless communication unit 10.
[0065] In more detail, in step 410, the signal processing unit 62
generates time domain data including at least one signal strength
set {RSS.sub.mn, . . . }.sub.TD in which an ID of the fixed node 2
that transmits each signal for each signal received in step 110, a
reception time point of each signal, and strength of each signal
measured in step 120 are grouped into one set. Here, RSS is an
abbreviation of "Received Signal Strength", TD is an abbreviation
of "Time Domain", a subscript "m" indicates a sequence number of
the ID of the fixed node 2, and "n" indicates a sequence number of
the reception time point of each signal.
[0066] For example, if the SLAM method illustrated in FIG. 3 is
repeatedly implemented three times, the scan unit 61 scans
peripheral signals three times. If the scan unit 61 receives only
one signal transmitted from the fixed node 2 having the second ID
when scanning a third signal, the time domain data includes only
one signal strength set RSS.sub.23. If the scan unit 61 receives a
signal transmitted from the fixed node 2 having the second ID and a
signal transmitted from the fixed node 2 having a third ID when
scanning the third signal scan, the time domain data includes the
signal strength set RSS.sub.23 and RSS.sub.33.
[0067] In this way, the time domain data can be regarded as data
for dividing the strength of each signal measured in step 120 into
an ID of the fixed node 2 transmitting each signal in a time domain
and a reception time point of each signal. Whenever the SLAM method
according to the present embodiment is implemented, the reception
time points of a plurality of signal strength sets {RSS.sub.mn, . .
. }.sub.TD included in the time domain data generated in step 410
are all the same. Accordingly, in order to reduce a length of the
time domain data, IDs of a plurality of fixed nodes and a plurality
of signal strengths may be arranged and attached to each other at
one time point for the signals collected at the same point or time.
It will be understood by those skilled in the art that the time
domain data can be represented in various formats other than the
above-described format.
[0068] In step 420, the domain conversion unit 63 of the wireless
localization unit 60 of the moving node 1 converts the time domain
data generated in step 130 into spatial domain data in which
strength of each signal measured in step 120 is represented in
association with the relative position of the moving node 1
estimated in step 220. In more detail, the domain conversion unit
63 converts the time domain data into at least one signal strength
set {RSS.sub.mn, . . . }.sub.SD in which IDs of the fixed nodes 2,
the relative position of the moving node 1, and the strengths of
each signal are grouped into one set by replacing reception time
point of each signal with the relative position of the moving node
1 corresponding to the reception time point of each signal, among
the IDs of the fixed nodes 2, the reception time point of each
signal, and the strength of each signal which is represented by
each set RSS.sub.mn for each set of at least one signal strength
set {RSS.sub.mn, . . . }.sub.TD included in the time domain data
generated in step 130.
[0069] Here, RSS is an abbreviation of "Received Signal Strength",
SD is an abbreviation of "Space Domain", a subscript "m" represents
a sequence number of the IDs of the fixed nodes 2, and "n"
represents a sequence number of the relative positions of the
moving node 1 corresponding to the sequence number of the reception
time points of each signal. In a case where reception of the signal
in step 110 and reception of the signal in step 210 are performed
at substantially the same time in synchronization with each other,
the relative positions of the moving node 1 corresponding to the
reception time points of each signal may be the relative positions
of the moving node 1 estimated in the reception time points of each
signal. In this case, the sequence number of the reception time
points of each signal is the sequence number of the relative
positions of the moving node 1 as it is. For example, the signal
strength set RSS.sub.23 included in the spatial domain data
indicates the strength of a signal received from the fixed node 2
having the second ID when the relative localization unit 50
estimates the third relative position.
[0070] If the reception of the signal in step 110 and the reception
of the signal in step 210 are not synchronized with each other, the
relative position of the moving node 1 corresponding to the
reception time point of each signal may be the relative position
estimated nearest to the reception time point of each signal among
the relative positions estimated in multiple time points. In this
way, the time domain data is time-based data in which the strength
of each signal is associated with the reception time point of each
signal by grouping the ID of the fixed node 2, the reception time
point of each signal, and the strengths of each signal into one
set, whereas the spatial domain data is a space-based data in which
the strength of each signal is associated with the relative
position of the moving node 1 by grouping the ID of the fixed node
2 included in the time domain data, the relative position of the
moving node 1 estimated in the time point included in the time
domain data, and the strength of each signal included in the time
domain data into one set.
[0071] Since the reception time points of a plurality of signal
strength sets {RSS.sub.mn, . . . }.sub.TD included in the time
domain data generated in step 410 are all the same each time the
SLAM method according to the present embodiment is implemented, the
relative positions of the plurality of signal strength sets
{RSS.sub.mn, . . . }.sub.SD included in the spatial domain data
converted in step 3420 are all the same each time the SLAM method
is implemented. Accordingly, in order to reduce a length of the
spatial domain data, IDs of a plurality of fixed nodes and
strengths of a plurality of signals may be arranged and attached to
one relative position for the signals collected at the same
relative position. It will be understood by those skilled in the
art that spatial domain data can be expressed in various formats
besides the above-described format.
[0072] in step 430, the pattern generation unit 64 of the wireless
localization unit 60 of the moving node 1 generates a change
pattern of at least one signal strength according to a relative
change of the position of the moving node over a plurality of time
points from the at least one signal strength measured in step 120
and the relative position of the moving node 1 estimated in step
230. In more detail, the pattern generation unit 64 generates a
pattern of at least one signal strength currently received in step
110 from at least one signal strength measured in step 120 and the
relative position of the moving node 1 estimated in step 230, and
successively arranges the pattern of the currently received at
least one signal in a pattern of at least one signal received
before the reception time point of the signal in step 110, thereby,
generating the change pattern of the at least one signal strength
according to the relative change of the position of the moving node
1 over a plurality of time points.
[0073] The SLAM method according to the present embodiment is a
method for repeatedly estimating a current position of the moving
node 1 in real time when the moving node 1 moves to a certain route
and simultaneously creating a map of the route where the moving
node 1 travels at the same time, and, while the SLAM apparatus
illustrated in FIG. 2 is being driven, the steps illustrated in
FIGS. 3 and 4 are continuously repeated. Since the moving node 1
cannot know signal strength distribution in a target region when
first passing through a certain route in the target region in order
to collect signal strengths to be stored in the storage 30, the
wireless localization according to the present embodiment is not
possible, and thus, execution of steps 130 and 310 is omitted.
[0074] That is, when the moving node 1 first passes through a
certain route in the target area, steps 210, 220, 230, 110, 120,
and 320 are repeatedly performed for the number of times of
relative localization on the route in order to collect the signal
strength at various localization points on the route and create a
signal intensity distribution map for the route. As a result, when
the moving node 1 first completes travel of a certain route in the
target area, the relative position value of the moving node 1
representing the route is stored in the storage 30 as a value in an
uncorrected state. When the moving node 1 completes a certain route
in the target area for the first time or when the moving node
completes the route several times, only whether or not the relative
position of the moving node 1 is corrected is different, but a
format of the map stored in the storage 30 is the same, and the map
stored in the storage 30 is updated by the number of times of
completion of the route.
[0075] More specifically, when the moving node 1 first passes
through a certain route in the target area, steps 210, 220 and 230
are performed to estimate a current relative position of the moving
node 1 and to perform steps 110 and 120 at the same time, and
thereby, strength of at least one signal received from the fixed
node 2 around the moving node 1 is measured. The moving node 1
stores the estimated relative position and the measured signal
strength in the storage 30 as signal strength at the relative
position. The moving node 1 creates a signal intensity distribution
map for the route by representing the route as a coordinate value
and signal strength of the relative position in each of a plurality
of localization points on the route estimated or measured while
taking a turn in the route. That is, if a process in which the
moving node 1 records the coordinate value and the signal strength
of the relative position at each localization point in a form of a
numerical value of a table or a graph of a three-dimensional space
in the storage 30 while sequentially passing through a plurality of
localization points on the route is completed for the entire route,
the storage 30 stores a signal intensity distribution map for the
route.
[0076] For example, in a case where the relative localization unit
50 estimates the relative position of the moving node 1 using a PDR
algorithm, each localization point on the route may be a step of
each user. In this way, since the relative position of the moving
node 1 is measured at a very short interval of approximately 1
meter, a continuous arrangement of a plurality of localization
points forms a specific route. The moving node 1 repeats the above
process for all the routes in the target area, and thereby, the
signal intensity distribution map for the entire target area is
completed and stored in the storage 30. According to the present
embodiment, the moving node 1 improves accuracy of coordinate
values of each localization point through a process in which the
moving node updates the coordinate values of each localization
point each time when travelling the route while travelling the
route several times, thereby optimizing a graph of the route
represented in a plurality of localization points so as to approach
an actual route.
[0077] FIG. 5 is a diagram illustrating a pattern formation
principle in step 430 of FIG. 4. Referring to (a) of FIG. 5, a
strength of a signal transmitted from the fixed node 2 is
attenuated approximately in inverse proportion to square of a
distance from the fixed node 2. In a case where a user approaches
and moves away from the fixed node 2, the moving node 1 carried by
the user receives a signal having the strength illustrated in (a)
of FIG. 5. In general, the user does not constantly walk at a
constant speed and may stop temporarily while walking. While the
user temporarily stops, even if the SLAM method illustrated in FIG.
3 is repeatedly implemented many times, the strength of the signal
transmitted from the fixed node 2 is measured approximately the
same as illustrated in (b) of FIG. 5. The x-axis in (b) of FIG. 5
represents a time point when the signal strength is measured, and
the y-axis represents the signal strength. The x-axis in (c) of
FIG. 5 represents a relative position (RL) of the moving node 1 and
the y-axis represents the signal strength.
[0078] Since the strength of the signal transmitted from the fixed
node 2 is measured each time the SLAM method illustrated in FIG. 3
is implemented, the strength of the signal transmitted from the
fixed node 2 is not represented in a continuous curve shape as
illustrated in (b) of FIG. 5, and is actually represented in a
shape in which dots represented at a height corresponding to the
strength of the signal are continuously arranged. If a reception
point to time of each signal is replaced with the relative position
of the moving node 1 by the domain conversion unit 63, change
patterns of the signal strength generated by the pattern generation
unit 64 are represented as continuous arrangement of the signal
strengths received a plurality of times at a plurality of relative
positions of the moving node 1 estimated at a plurality of time
points as illustrated in (c) of FIG. 5. Accordingly, it can be said
that the change pattern of at least one signal strength generated
by the pattern generation unit 64 is a change pattern of at least
one signal strength represented as continuous arrangement of at
least one signal strength received a plurality of times.
[0079] The storage 30 stores a map in a form of a table
representing distribution of signal strengths collected in the
target area according to the SLAM method according to the present
embodiment, or a map of a graphic shape representing a distribution
pattern of signal strength. When a user repeatedly moves through
the same route several times, times necessary for moving the entire
route is generally different from each other. In a case where
movement routes of a user are the same, even if the times necessary
for moving the entire route are different, several positions of the
user on the route are the same. Accordingly, reflecting a reception
time point of the signal transmitted from the fixed node 2 in the
map is not only impossible, but also unnecessary. That is, the map
according to the present embodiment is represented by an ID of the
fixed node 2 from which a signal is transmitted with respect to
many signals collected in the entire target region, a position of a
point at which the signal is received, and a signal strength
distribution to which the signal strengths are reflected.
[0080] In order to estimate the reception position of the moving
node 1 for the signal received at a current time point in
accordance with the present embodiment, a pattern that can be
matched to the signal strength distribution map has to be
generated. Since localization of the moving node 1 is performed in
a state where a position of the moving node 1 is not known, the
moving node 1 generates time domain data representing each signal
strength in association with a reception time point of each signal,
and thereafter, converts the time domain data into spatial domain
data in which each signal strength is associated with the relative
position of the moving node 1 corresponding to the reception time
point of each signal. In order to determine coordinates of the map,
a target region of the real world in which the moving node 1 moves
around is divided into a grid structure in which distances between
scales are constant. Since a value of a position of a certain point
on the map is represented by two-dimensional coordinates having a
resolution of this unit.
[0081] As illustrated in (c) of FIG. 5, as a user is in a
temporarily stopped state, a plurality of dots representing the
strength of a plurality of signals received at a plurality of
relative positions of the moving node 1 may be concentrated. In
this case, if a maximum distance between the plurality of
concentrated dots is within a distance corresponding to a
coordinate resolution unit of the map, that is, a resolution unit
of coordinates for representing the relative position of the moving
node 1, there is an effect that the plurality of concentrated dots
represent one signal strength as one dot, which causes a change
pattern of the signal strength to be generated. For example, if the
coordinate resolution unit of the map is 1 meter, there is an
effect that several dots concentrated within one meter represent
one signal strength as one dot, which causes a change pattern of
the signal strength to be generated.
[0082] In step 430, the pattern generation unit 64 generates a
pattern of at least one signal strength received from at least one
fixed node 2 at a relative position of the moving node 1 estimated
in step 230, from the spatial domain data converted in step 420. In
step 323, the pattern of at least one signal strength generated by
the pattern generation unit 64 is a pattern of at least one signal
strength generated by representing at least one signal strength
represented by spatial domain data for at least one fixed node
represented by the spatial domain data at a relative position
represented by the spatial domain data of a movement route of the
moving node 1. In step 323, the pattern generation unit 64
generates the pattern of at least one signal strength by generating
a signal strength graph representing a signal strength of each
signal strength set RSS.sub.mn for each signal strength set
RSS.sub.mn of at least one signal strength set {RSS.sub.mn, . . .
}.sub.SD included in the spatial domain data received in step
310.
[0083] FIG. 6 is a diagram illustrating a three-dimensional spatial
coordinate system for generating a change pattern of a signal
strength used for the SLAM method according to the present
embodiment. Referring to FIG. 6, the x-axis of a three-dimensional
space is a coordinate axis in which IDs of a plurality of fixed
nodes 2 are arranged at a regular interval, the y-axis is a
coordinate axis in which a movement route of the moving node 1 is
divided into resolution units of coordinates for representing the
relative position of the moving node 1, and the z-axis is a
coordinate axis in which a measurement range of the strength of a
signal received from the plurality of fixed nodes 2 is divided into
measurement resolution units of the signal strength. It will be
understood by those skilled in the art that information represented
by each of the x-axis, the y-axis, and the z-axis of the
three-dimensional space can be exchanged with each other. For
example, the x-axis may represent the relative position of the
moving node 1, and the y-axis may represent the ID of the fixed
node 2.
[0084] The three-dimensional spatial coordinate system illustrated
in FIG. 6 is based on the assumption that a movement route of a
user or a vehicle is determined as in a case of a road in the
center of a city, and in a case where a signal strength
distribution map stored in the storage 30 is built based on
collected signals while moving along a route determined as such, a
signal strength distribution of a map includes a movement route
represented in arrangement of a signal reception positions. That
is, in a case where the change pattern of a current signal strength
of the moving node 1 coincides with a pattern of a certain part of
the signal strength pattern in the map, it is possible to know a
point of the movement route where the moving node 1 is located by
comparing with the signal strength distribution in the map. In a
case where the movement route of the moving node 1 is not
determined or a height of the moving node 1 is estimated in
addition to the position of the moving node 1 on the ground, It may
be necessary to generate a change pattern of at least one signal
strength received in step 110 for multi-dimensional spatial
coordinate system higher than four-dimensional spatial coordinate
system.
[0085] In order to facilitate understanding of the present
embodiment, ten access points corresponding to the fixed node 2 of
a Wi-Fi network are arranged in the x-axis of FIG. 6, and users
carrying the moving nodes 1 are arranged at a length of 10 meter at
intervals of 1 meter. Accordingly, the resolution unit of the
relative position coordinates of the moving node 1 is 1 meter. As
described below, the change pattern of the signal strength compared
with a signal strength distribution map stored in the storage 30 in
step 440 is a three-dimensional pattern generated in the
three-dimensional space of a size illustrated in FIG. 6. That is,
the size of the three-dimensional space illustrated in FIG. 6 means
that a change pattern of signal strength compared with the signal
strength distribution map stored in the storage 30 is generated at
intervals of 10 meters with respect to a route where the moving
node 1 moves during the localization according to the present
embodiment. At this time, the number of access points on the
movement route of the moving node 1 is 10. The three-dimensional
spatial coordinate system illustrated in FIG. 6 is only an example,
and the number of access points and the length of the movement
route of the moving node 1 may be variously modified and
designed.
[0086] In step 430, the pattern generation unit 64 generates a
graph illustrating the signal strength of the signal strength set
RSS.sub.mn in such a manner that a dot is marked on a point of a
three-dimensional space determined by mapping an ID of the fixed
node represented by any one of the signal strength sets RSS.sub.mn
for each signal strength set RSS.sub.mn included in the spatial
domain data converted in step 420 on the x-axis of a
three-dimensional space, mapping the relative position of the
moving node 1 represented by the strength set RSS.sub.mn on the
y-axis, and mapping strength of the signal represented by the
signal strength set RSS.sub.mn on the z-axis. The signal strength
graph is not an image output graph to be shown to a user, but is a
graphical element at an intermediate stage for illustrating a
process of generating a change pattern of a signal strength in the
form of a three-dimensional graph used for wireless localization.
However, in order to facilitate understanding of the present
embodiment, description will be hereinafter made below by assuming
that a signal strength graph for each signal strength set
RSS.sub.mn, a pattern of the signal strength at a relative
position, and a change pattern of a signal strength according to a
change in the relative position can be visually recognized.
[0087] As described above, the pattern of at least one signal
strength generated by the pattern generation unit 64 means a
pattern of at least one signal strength representing at least one
signal strength represented by the spatial domain data in
accordance with an ID of at least one fixed node represented by the
spatial domain data and a relative position represented by the
spatial domain data. Accordingly, if the moving node 1 receives
only one signal, the pattern of the signal strength at the relative
position of the moving node 1 estimated in step 230 may be one dot
shape. If the moving node 1 receives a plurality of signals, the
pattern of the signal strength at the relative position of the
moving node 1 estimated in step 230 may be a linear line shape or a
curved shape represented by a plurality of adjacent dots.
[0088] In step 430, the pattern generation unit 64 accumulates
pattern data representing the pattern of at least one signal
strength generated in this way on the pattern data stored in the
buffer 40 and store the accumulated data. The pattern data stored
in the buffer 40 is pattern data with respect to a relative
position estimated before the relative position is estimated in
step 230. The change pattern of at least one signal strength
measured in step 120 is generated by accumulating the pattern data.
The pattern data necessary for generating the change pattern of the
signal strength compared with a signal strength distribution of the
map stored in the storage 30 can be accumulated in the buffer 40,
and a larger amount of pattern data can be accumulated. In the
latter case, the change pattern of the signal strength is generated
from a part of the pattern data accumulated in the buffer 40.
[0089] FIGS. 7A and 7B are table forms illustrating the
accumulation of pattern data used for the SLAM according to the
present embodiment. In FIG. 7A, the pattern data accumulated in the
buffer 40 is represented in a table form. In step 430, the pattern
generation unit 64 may accumulate the spatial domain data in the
buffer 40 in the table form of FIG. 7A. In the table of FIG. 7A, a
value "m" of "APm" corresponds to coordinate values of the x-axis
in a three-dimensional space as a sequence number of IDs of the
fixed nodes 2, a value "n" of "RLn" corresponds to coordinate
values of the y-axis in the three-dimensional space as a sequence
number of relative positions of the moving node 1, and "RSS.sub.mn"
corresponds to coordinate values of z-axis in the three-dimensional
space as strengths of signals which are transmitted from the fixed
nodes 2 having IDs "APm" and are received at relative positions
"RLn" of the moving node 1.
[0090] According to the pattern generating method of the pattern
generation unit 64 described above, since a dot is represented at a
height corresponding to the value "RSS.sub.mn" at a point of a
two-dimensional plane determined by the value "m" of "APm" and the
value "n" of "RLn", a set of "RSS.sub.mn" illustrated in FIG. 7A
forms a geometric surface in the three-dimensional space. As
described above, in step 430, the pattern generation unit 64
generates a three-dimensional pattern of a geometric surface shape
that graphically representing a change of at least one signal
strength according to a relative change of a position of the moving
node 1 in such a manner that a dot is marked on a point of the
three-dimensional space determined by mapping the ID of one fixed
node on the x-axis of the three-dimensional space, mapping the
relative position of the moving node 1 on the y-axis, and mapping
the strength of a signal which is transmitted from the fixed node
and is received at the relative position on the z-axis. A plurality
of signal strength sets included in the spatial domain data
accumulated in the buffer 40 may not accumulate in the buffer 40 in
the table form of FIG. 7A and may be accumulated in the buffer 40
in various forms for efficient use of a memory space.
[0091] FIG. 8 is a diagram illustrating an example of a target
region of map creation according to the SLAM of the present
embodiment. In the example of FIG. 8, the target area of map
creation according to the SLAM of the present embodiment is an
indoor space of a building, and a corridor of the building has a
rectangular perimeter shape. A user started from a starting point
marked by a dot in FIG. 8 carrying a smartphone which is the moving
node 1 and arrived at the starting point by taking a turn in the
corridor counterclockwise. At this time, the moving node 1 starts
from the starting point (0, 0) and moves along dotted lines
illustrated in FIG. 8. In the present embodiment, a position of the
moving node 1 is represented by a two-dimensional coordinate value
of a two-dimensional coordinate system.
[0092] Since the moving node 1 determines a direction where the
moving node proceeds based on compass information, in a case where
a side of the building is inclined with respect to the north-south
direction of a compass, a plane of the building may be displayed on
a two-dimensional coordinate system of a map created by the moving
node 1 in a tilted state as illustrated in FIG. 8. The
three-dimensional spatial coordinate system illustrated in FIG. 6
has to be distinguished from a two-dimensional coordinate system of
a map created according to the SLAM algorithm according to the
present embodiment, as a spatial coordinate system for generating a
signal intensity change pattern of a three-dimensional surface
shape. That is, a value of the relative position of the moving node
1 mapped to the y-axis of the three-dimensional space illustrated
in FIG. 6 is represented by a coordinate value of the
two-dimensional coordinate system illustrated in FIG. 8.
[0093] The SLAM or related art estimated the position of the moving
node based on several physical landmarks. Examples of the landmarks
are a door, a stair, and the like of a building having a
characteristic shape without changing a position thereof. A sensor
such as a LiDAR, a camera, or an ultrasonic sensor is required to
identify the landmarks. The LiDAR is very high in resolution, but
is expensive and there is a limit to miniaturization, thereby,
being difficult to be applied to a small moving node (1) such as a
smartphone. In the same manner, It is difficult for a camera to be
applied to a smartphone having a low image data processing
capability because an output thereof is image data. An ultrasonic
sensor can be miniaturized, but has very low resolution, and
thereby, there is a limit to creating a map with high accuracy. The
SLAM according to the present embodiment does not require a
landmark described above, unlike the SLAM of related art, and
instead. similarity between the signal intensity change pattern
generated by the pattern generation unit 64 and a corresponding
pattern in the signal strength distribution of a map stored in the
storage 30 serves as a kind of landmark.
[0094] In this way, since the present embodiment can estimate the
position of the moving node 1 based on the similarity of a pattern
with a very low complexity as compared with image processing of the
SLAM of related art and generate a map at the same time, the SLAM
method according to the present embodiment can be implemented
smoothly without a separate sensor for identifying a physical
landmark even in the light, thin, short, and small moving node 1.
Particularly, a map created by the SLAM method according to the
present embodiment has an advantage that can be used for wireless
localization in general because the map includes a signal intensity
distribution corresponding to a kind of radio map in addition to a
physical terrain such as a route of a target area.
[0095] As described above, since the moving node 1 cannot know a
signal intensity distribution inside the building when taking a
turn in the corridor of the building illustrated in FIG. 8 for the
first time, wireless localization cannot be performed, and thus,
execution of step 130 and 310 of FIG. 3 is omitted. In the example
illustrated in FIG. 8, the moving node 1 moves according to walking
of a user, and thereby, the relative localization unit 50 estimates
a relative position of the moving node 1 using the PDR algorithm,
and each localization point on the route becomes each step of the
user. That is, the moving node 1 estimates the relative position of
the moving node 1 at each step of the user by performing steps 210,
220. and 230 for each step of the user, and simultaneously measures
strength of at least one signal received at each step position of
the user by performing steps 110 and 120. The moving node 1
represents a corridor route as a coordinate value of a relative
position and signal strength in each of a plurality of steps on the
corridor route while taking a turn in the corridor of the building,
thereby creating a signal intensity distribution map for the
route.
[0096] As a result, the storage 30 stores a signal intensity
distribution map for a route. Unlike the example illustrated in
FIG. 8, the signal intensity distribution map can be created and
stored in the storage 30 in the same manner for each route even in
a case where there are multiple routes in the target area. Since it
can be seen that the signal intensity distribution map represents a
change pattern of signal intensity only in a certain route, the map
data stored in the storage 30 can be generated in the same format
as the pattern data accumulated in the buffer 40. In this way, if a
format of the pattern data accumulated in the buffer 40 is the same
as a format of the map data stored in the storage 30, the pattern
data accumulated in the buffer 40 and the map data stored in the
storage 30 are coincide with each other.
[0097] In FIG. 7B, the map data stored in the storage 30 is
represented in a form of a table having the same format as the
format illustrated in FIG. 7A. A value "m" of "APm" is a sequence
number of an ID of the fixed node 2 installed in the target area, a
value "n" of "ALn" is a sequence number of a reception position of
the moving node 1, and "RSS.sub.mn" is strength of a signal
transmitted from the fixed node 2 having an ID of "APm" and
received at the reception position "ALn" of the moving node 1, in
the table of FIG. 7B. Since the reception position of the moving
node 1 is estimated not by a relative position with respect to
other positions of the moving node 1 but by a position where a
corresponding pattern coinciding most closely with a change pattern
of the signal strength generated by the pattern generation unit 64
is located in the signal intensity distribution map stored in the
storage 30, the reception position of the moving node 1 can be
referred to as a kind of an absolute location (AL) in terms of
being in contrast with the relative position of the moving node 1.
Accordingly, the reception position of the moving node 1 is denoted
as an absolute position in the table of FIG. 7B.
[0098] As described above, the format of the map data stored in the
storage 30 is the same as the format of the pattern data
accumulated in the buffer 40. Accordingly, description of the map
data stored in the storage 30 will be replaced with description of
the pattern data of the buffer 40 described above. Since the signal
intensity distribution map generated according to the SLAM
algorithm according to the present embodiment includes a kind of
radio map created by storing strengths of many signals collected in
the target region in a database, a value "RSS.sub.mn" of FIG. 7B is
represented as a specified value.
[0099] FIG. 9 is a diagram illustrating an example in which the
change pattern of the signal strength used for the SLMA according
to the present embodiment is generated. When a user moved by 20
meters along the corridor of the building illustrated in FIG. 8
under the assumption that a scale of the three-dimensional spatial
coordinate system illustrated in FIG. 9 is 10 times a scale of the
three-dimensional spatial coordinate system illustrated in FIG. 6,
the relative position of the moving node 1 is estimated 20 times
and a three-dimensional pattern of a surface shape corresponding to
the movement distance is generated by a pattern at each of the 20
relative positions, according to a pattern generation technique of
the pattern generation unit 64 described above. A surface
illustrated in FIG. 9 is formed by concentrated dots of heights
different from each other. It can be seen that, when a user moves
40 meters, 60 meters, and 80 meters, the three-dimensional pattern
of the surface shape is expanded by the amount of addition of the
movement distance. A curvature of the surface is generated due to a
strength difference between signals transmitted from the adjacent
fixed nodes 2, that is, a difference between adjacent
"RSS.sub.mn".
[0100] In step 440, the comparison unit 65 of the wireless
localization unit 60 of the moving node 1 compares a change pattern
of at least one signal strength generated in step 430 with signal
strength distribution of a map stored in the storage 30, thereby,
searching a part having a pattern most similar to the change
pattern of at least one signal strength generated in step 430
within the signal strength distribution of the map stored in the
storage 30. As described above, the signal strength distribution of
the map stored in the storage 30 indicates signal strength
distribution in a region where the moving node 1 is located, that
is, a region which becomes a target of map creation. More
specifically, the comparison unit 65 compares a three-dimensional
pattern of a geometric surface shape graphically representing a
change of at least one signal strength generated in step 430 with a
three-dimensional pattern of a geometric surface shape graphically
representing the signal strength distribution of the map stored in
the storage 30, thereby, searching a surface part having a shape
most similar to the surface shape of the three-dimensional pattern
graphically representing the change of at least one signal strength
generated in step 430 within the signal strength distribution of
the map stored in the storage 30.
[0101] In a case where the map data stored in the storage 30 is
represented in the form of the table of the format illustrated n
FIG. 7B, a three-dimensional pattern of the geometric surface shape
graphically representing the signal intensity distribution of a map
needs to be generated from the map data stored in the storage 30.
In this case, the pattern generation unit 64 generates a
three-dimensional pattern in a form of a geometric surface shape
graphically representing a change of at least one signal intensity
according to a relative change in a position of the moving node 1,
and simultaneously generates a three-dimensional pattern of
geometric surface shape graphically representing the signal
intensity distribution of the map stored in the storage 30. That
is, the pattern generation unit 64 generates a three-dimensional
pattern of a geometric surface shape graphically representing
signal intensity distribution of the map stored in the storage 30
in such a manner that dots are displayed at points in a
three-dimensional space determined by mapping IDs of one fixed node
on the x-axis of a three-dimensional space, mapping absolute
positions having signal strength information in the map on the
y-axis, and mapping strength of a signal which is transmitted from
the fixed node and is received at the absolute position on the
z-axis with reference to the table illustrated in FIG. 7B.
[0102] The pattern generation unit 64 may generate a
three-dimensional pattern of a geometric surface shape graphically
representing all or a part of the signal intensity distribution of
a map stored in the storage 30. In a case where a size of the
target region is small, even if all of the signal intensity
distribution of the map stored in the storage 30 is represented as
a graph, there is almost no problem in the data processing of the
moving node 1. In a case where the size of the target region is
large, if all of the signal intensity distribution of the map
stored in the storage 30 is represented as a graph, the moving node
1 may take a heavy load. The pattern generation unit 64 can set a
pattern region including a current position of the moving node 1
based on the ID of at least one fixed node 2 that transmits the
signals scanned by the scan unit 61, within the map stored in the
storage 30, and can generate a three-dimensional pattern of a
geometric surface shape graphically representing the signal
intensity distribution of the pattern regions set in this way. The
size of the pattern region has to be appropriately set such that an
error does not occur in the estimated value of the reception
position of the moving node 1 due to the size thereof without
greatly influencing the load of the moving node 1.
[0103] As described above, the present embodiment determines where
the change pattern of at least one signal strength generated in
step 430 is located within the signal strength distribution of the
map stored in the storage 30, based on a surface correlation
between the change pattern of at least one signal strength
generated in step 430 and the distribution pattern of the signal
strength of the map stored in the storage 30. For example, the
surface correlation may be calculated by using a three-dimensional
shape matching algorithm known to those skilled in the art to which
the present embodiment belongs.
[0104] In step 450, the reception position estimation unit 66 of
the wireless localization unit 60 of the moving node 1 estimates a
reception position of the moving node 1 for at least one signal
received in step 110, based on a comparison between the change
pattern of the signal strength generated in step 430 and the signal
intensity distribution in the region where the moving node 1 is
located. Here, the signal intensity distribution in the region
where the moving node 1 is located is the signal intensity
distribution of the map stored in the storage 30. More
specifically, in step 450, the reception position estimation unit
66 estimates an absolute position in the region where the moving
node 1 is located, which is indicated by the part searched by the
comparison in step 440, that is, the estimated surface part, as the
reception position of the moving node (1) for at least one signal
received in step 110. Here, the absolute position in the region
where the moving node 1 is located indicates a position in the
coordinate system of the signal intensity distribution map stored
in the storage 30.
[0105] In this way, the present embodiment does not consider only
the currently received signal strength like the related art, but
estimates the reception position of the moving node 1 using the
change pattern of at least one signal strength according to the
relative change of the position of the moving node 1 over the
plurality of time points so far unlike the related art, and
thereby, if a length of the change pattern of the signal strength
is set to be very long, a real-time nature of the localization of
the moving node 1 may be degraded. However, a shape similarity
between the surface representing the change pattern of the signal
strength up to a current position of the moving node 1 and the
surface representing a pattern of the signal strength distribution
of the map stored in the storage 30 can be rapidly determined by
using the three-dimensional shape matching algorithm, and thereby,
the real-time nature of the localization of the moving node 1 may
be guaranteed even in a case where the length of the change pattern
of the signal strength over the plurality of time points is very
long.
[0106] FIGS. 10A to 11B are diagrams illustrating examples in which
a reception position of the moving node 1 is estimated in
accordance with the SLAM algorithm according to the present
embodiment. Scales of the three-dimensional spatial coordinate
system illustrated in FIGS. 10A to 11B are the same as the scale of
the three-dimensional space coordinate system illustrated in FIG.
6, and pattern examples based on the relative positions of the
moving node 1 illustrated on the left side of FIGS. 10A to 11B are
the same as the example illustrated in FIG. 9. Pattern example of
based on the absolute positions of maps illustrated on the right
side of FIGS. 10A to 11B illustrate maps of the distribution
pattern of the signal strength for a movement route up to 100
meters. A map stored in the storage 39 is much larger than the maps
illustrated on the right side of FIGS. 10A to 11B, but only a part
relating to the matching with the patterns illustrated on the left
side of FIGS. 10A to 11B in the map stored in the storage 30 is
illustrated on the right side of FIGS. 10A to 11B due to limitation
of a size of paper. When a user moves by 20 meters, a
three-dimensional pattern of a surface shape illustrated on the
left side of FIG. 10A is generated.
[0107] According to a matching method based on the surface
correlation described above, the comparison unit 65 searches a
boldly-marked part in the pattern map illustrated on the right side
of FIG. 10A. Likewise, when a user moves 40 meters, 60 meters, and
80 meters, three-dimensional patterns of a surface shape
illustrated on the left sides of FIG. 10B, FIG. 11A, and FIG. 11B
are sequentially generated. The comparison unit 65 sequentially
searches the boldly-marked parts in the pattern maps illustrated on
the right sides of FIG. 9B, FIG. 100, and FIG. 10D. The reception
position estimation unit 66 estimates the relative position
estimated in step 230 among a plurality of absolute positions of a
part found out in step 440, that is, a plurality of absolute
positions of the surface part, that is, the absolute position
corresponding to the last estimated relative position, as the
reception position of the moving node 1. A correspondence
relationship between the relative position and the absolute
position is determined from a shape matching relationship between
the two surfaces. That is, the reception position estimation unit
66 estimates an absolute position of the part having a shape most
similar to the shape of the relative position estimated in step 230
among a plurality of absolute positions of the surface part found
out in step 440, as the reception position of the moving node
1.
[0108] Various wireless localization algorithms including a
k-nearest neighbor (KNN) algorithm widely known as a wireless
localization technology of related art, a particle filter
algorithm, and an algorithm obtained by combining a particle filter
and PDR estimate the position of the moving node 1 in common using
only the currently received signal strength. In a case where a
signal strength different from the signal strength received at the
time of creating a map is measured due to a wireless environment
change such as signal interference between communication channels,
expansion of an access point, and occurrence of a failure or an
obstacle, points adjacent to each other in the map have a similar
signal strength distribution, and thereby, the wireless
localization algorithm of related art has a very high probability
that a current position of the moving node 1 is estimated to be an
adjacent position other than an actual position thereof. The larger
the difference between the strength of the signal received at the
time of creating the map and the strength of the currently received
signal, the greater the localization error.
[0109] As described above, the SLAM algorithm according to the
present embodiment estimates the reception position of the moving
node 1 using the change pattern of at least one signal strength
according to the relative change of the position of the moving node
over a plurality of time points, and thereby, an error of the
estimated value of the reception position of the moving node 1
rarely occurs, even if there occurs a wireless environment change
such as signal interference between communication channels,
expansion of an access point, and occurrence of a failure or an
obstacle. That is, the SLAM algorithm according to the present
embodiment estimates the current position of the moving node 1
receiving a certain signal, based on the change pattern of the
signal strength, in consideration of not only the strength of the
currently received signal but also all the past signal strengths
received in the route where the moving node 1 passes through so
far, and thereby, the wireless environment change in the current
position of the moving node 1 rarely influences the estimation in
the current position of the moving node 1.
[0110] The adjacent point of the actual position of the moving node
1, which is estimated when only the strength of the currently
received signal is considered due to the wireless environment
change according to the wireless localization algorithm of related
art, becomes a point deviating from the route represented by the
change pattern of the signal strength so far. According to the
present embodiment, the wireless environment chant in the position
where the moving node 1 is currently located is not able to change
the entire change pattern of the signal strength received in the
route where the moving node 1 passes through so far and changes a
current time point of such a pattern. Accordingly, if a position of
the moving node 1 is estimated by using a change pattern of at
least one signal strength according to a relative change of a
position of a moving node over a plurality of time points so far,
there is a high possibility that an actual position of the moving
node 1 receiving a current signal is estimated as a reception
position of the moving node 1 rather than an adjacent position of
the actual position of the moving node 1 estimated according to the
wireless localization algorithm of related art. Of course, if the
wireless environment change continuously occurs at various points
on a movement route of the moving node 1, a localization error may
occur, but this case rarely occurs.
[0111] Particularly, a strength of a signal received from a certain
fixed node 2 reaches a peak when going around the fixed node, and
the peak tends to be rarely influenced by the wireless environment
change. Accordingly, if a length of the change pattern of the
signal strength used for the SLAM according to the present
embodiment is sufficiently lengthened within a limitation where a
real-time nature of the localization is guaranteed such that the
currently received signal includes peak parts of various signals on
the route where the moving node 1 passes previously although not a
part adjacent to the peak, the peak becomes very robust to the
wireless environment change.
[0112] As described above, the change pattern of the signal
strength used for the SLAM of the present embodiment is a
three-dimensional pattern of a geometric surface shape graphically
representing a change of at least one signal strength according to
a relative change of a position of the moving node 1, and, when
viewing from a viewpoint of comparison between a three-dimensional
pattern of a surface shape of the moving node 1 and a
three-dimensional pattern of a surface shape of map data, the
wireless environment change in a current position of the moving
node 1 results in a height error only of a surface part
corresponding to the strength of the currently received signal, and
does not influence most of the surfaces corresponding to points
other than a point of the wireless environment change. That is, the
wireless environment change in the current position of the moving
node 1 does not substantially influence the entire shape of the
surface, although causing some deformation of the surface
shape.
[0113] Since the wireless localization algorithm of related art
compares a numerical value of a currently received signal strength
with a numerical value of a signal strength distributed in a radio
map, it leads to a result that a point adjacent to the actual
position of the moving node 1 having a numerical value most similar
to the numerical value of the currently received signal strength is
wrongly estimated as a position of the moving node 1. According to
the SLAM algorithm according to the present embodiment, the
wireless environment change in the current position of the moving
node 1 rarely influences the entire shape of the surface, and
thereby, when a surface part having the shape most similar to the
surface shape of the three-dimensional pattern is found out in the
map represented by the map data, there is a very low possibility
that a surface part different from the surface part to be
originally found out is found out due to an error of a strength of
the currently received signal. As described above, the localization
error of the algorithm of related art according to the comparison
between the numerical value of the currently received signal
strength and the numerical value of the signal strength distributed
in the radio map can be originally blocked, and thereby,
localization accuracy of the moving node 1 can be greatly
improved.
[0114] Since a base station of an LTE network costs much more than
an access point of a Wi-Fi network to install, the base station is
installed as far as possible from an adjacent base station so as
not to overlap a relay service region thereof. As a result, there
are characteristics that LTE signals are uniformly distributed
throughout indoor and outdoor spaces, but a region where a change
of the signal strength is not large is wide.
[0115] As described above, since the wireless localization
algorithm of related art estimates a position of the moving node 1
using only the currently received signal strength in common, in a
case where there is almost no change in the signal strength between
the localization points on a movement route of the moving node 1,
not only the localization points cannot be distinguished only by
the signal strength, but also the signal strength is very sensitive
to peripheral noise, and thereby, a localization error becomes very
larger.
[0116] Even in a case where a strength of the LTE signal rarely
changes between adjacent localization points on a movement route of
the moving node 1, if a length of a change pattern of a signal
strength used for the wireless localization of the present
embodiment is sufficiently lengthened within a limitation where a
real-time nature of localization of the moving node 1 is
guaranteed, a strength of the LTE signal is sufficiently changed to
the extent that an accurate position estimation of the moving node
1 can be performed within a movement distance corresponding to a
length of a change pattern of the signal strength. Accordingly,
even in a case where there is almost no change in the strength of
the LTE signal between the adjacent localization points on the
movement route of the moving node 1, the SLAM algorithm according
to the present embodiment can accurately estimate the position of
the moving node 1.
[0117] In this wat, the SLAM algorithm according to the present
embodiment can accurately estimate a position of the moving node 1
by using an LTE signal with almost no change in a signal strength
between measurement points on a movement route, thereby, being able
to create a map which can cover both an indoor space and an outdoor
space. As a result, the SLAM algorithm according to the present
embodiment can provide a map which can perform a highly accurate
indoor localization and outdoor localization even in the center of
a city without being influenced by a skyscraper by using LTE
signals widely distributed in the inside of a building and the
center of a city, thereby, being able to be replaced with a map
based on the GPS which is widely used as a car navigation system
nowadays but cannot be used for indoor localization and of which
localization accuracy is significantly degraded in the center of a
city, and being able to accelerate realization of fully autonomous
driving including an outdoor space and an indoor space.
[0118] In the above description, in a case where a WiFi signal and
an LTE signal are used, superiority of localization accuracy of the
SLAM algorithm according to the present embodiment is described,
but signals that can be used for the SLAM of the present embodiment
are not limited to this, and the localization according to the SLAM
of the present embodiment can be performed by using strength of a
radio signal such as Bluetooth, Zigbee, Lora, or the like.
[0119] In step 310, the position correction unit 70 of the moving
node 1 corrects the relative position of the moving node 1
estimated in step 30, based on a comparison between a change
pattern of the signal strength generated in step 430 and
distribution of the signal strength in a region where the moving
node 1 is located. Here, the signal strength distribution in the
region where the moving node 1 is located is a signal intensity
distribution of the map stored in the storage 30. More
specifically, in step 310, the position correction unit 70 corrects
a coordinate value of the relative position of the moving node 1
estimated in step 230 by using a coordinate value of a reception
position of the moving node 1 estimated in step 450, thereby,
correcting the relative position estimated in step 230. In the
present embodiment, correction of a position or correction of a
coordinate value means that the position or the coordinate value is
optimized so as to approximate an actual position or an actual
coordinate value, which does not necessarily mean that the position
or the coordinate value is changed.
[0120] Generally, since a position in a localization field is
estimated based on a probability model, there is always an error in
an estimated value of the position. For example, a point where the
moving node 1 is actually located at is not known even by the
relative localization and the wireless localization used for the
SLAM according to the present embodiment, but the point is
estimated as a position of the moving node 1 on the assumption that
probability of being located at the point is high. As described
above, the relative position of the moving node 1 estimated in step
230 is very low due to error accumulation and the like. The present
embodiment corrects the relative position estimated in step 230,
based on a comparison between the change pattern of the signal
strength generated in step 430 and the distribution of the signal
strength in the region at which the moving node 1 is located,
thereby, reducing an error of the relative position of the moving
node 1.
[0121] FIGS. 12A to 12C are movement trajectory diagrams of the
moving node 1 estimated by a PDR algorithm in the target region
illustrated in FIG. 8. FIG. 12A illustrated a movement trajectory
of the moving node 1 estimated by the PDR algorithm, while a user
holds a smartphone, starts from a starting point (0,0), and turns a
corridor of a building twice. From this, it can be seen that the
trajectory estimated at the first turn and the trajectory estimated
at the second turn are very different from each other.
Particularly, it can be seen that the trajectory estimated at the
second turn is far different from an actual trajectory of the
moving node 1 due to accumulation of errors of the PDR. FIG. 12B
illustrates a state where localization points having similar
strengths of signals received at the respective localization points
in the first movement trajectory and the second movement trajectory
are connected to each other, and FIG. 12C illustrates a state where
localization points having similar change patterns of signal
strengths generated at the respective localization points in the
first movement trajectory and the second movement trajectory are
connected to each other.
[0122] It can be seen from FIG. 12B that, in a case where a signal
received at a certain localization point is in an abnormal state
such as a state where the signal is very weak or deformed, a
strength error of the signal received at the localization point is
very large, and thereby, the localization points corresponding to
actual same positions in the first movement trajectory and the
second movement trajectory are not connected to each other and
wrong localization points are connected to each other. It can be
seen from FIG. 12C that, even in a case where a signal received at
a certain localization point is in an abnormal state such as a
state where the signal is very weak or deformed, an entire shape of
the change pattern of the signal strengths generated at the
respective localization points is rarely influenced, and thereby,
localization points corresponding to actual same points in the
first movement trajectory and the second movement trajectory are
relatively accurately connected to each other.
[0123] Accordingly, it can be seen that an error of a relative
position of the moving node 1 is accurately corrected over the
entire target region, when a coordinate value of the relative
position of the moving node 1 is corrected by using a coordinate
value of a reception position of the moving node 1 estimated by a
wireless localization algorithm based on the surface correlation
according to the present embodiment, compared with a case where the
coordinate value of the relative position of the moving node 1 is
corrected by using a coordinate value of a reception position of
the moving node 1 estimated by the wireless localization algorithm
of related art. As a result, localization accuracy of the moving
node 1 is improved, and thereby, it is possible to create a map in
which accurate route information is recorded.
[0124] Particularly, since the wireless localization unit 60
estimates the reception position of the moving node 1 by using the
wireless localization based on the surface correlation using the
change pattern of at least one signal strength received over a
plurality of time points, an error in an estimated value of the
reception position of the moving node 1 rarely occurs, even if
there is a change in an wireless environment, such as a signal
interference between communication channels, expansion of an access
point, occurrence of failure or an obstacle. In the present
embodiment, the coordinate value of the relative position of the
moving node 1 estimated in step 230 is corrected by using the
coordinate value of the reception position of the moving node 1
estimated in step 450, and thereby, a current position of the
moving node 1 can be accurately estimated by correcting the
relative position of the moving node 1 even in a case where a
wireless environment change is made.
[0125] A reason why the accuracy of wireless localization based on
the surface correlation is very high is that an error of the signal
received at a current time point can be corrected by a change
pattern of the signals received at various time points in the past
even in a case where the signal received at the current time point
is in an abnormal state such as a case where the signal is very
weak or deformed because since the surface correlation uses a
change pattern of at least one signal strength received over a
plurality of time points. Meanwhile, when the moving node 1 is
located at a branch where one path is divided into several branches
or a point where the moving node enters from a narrow alley into a
wide square, change patterns of the signals received over various
time points in the past are all the same with respect to an
arbitrary route in several paths or in the square, and thereby, It
is very difficult to estimate into which path the moving node 1
enters among the several paths or in which direction the moving
node 1 proceeds in the square, in a case where the signal received
at the current time point is in an abnormal state such as a case
where the signal is very weak or deformed.
[0126] Accordingly, in a case where a route change occurs, for
example, one path is divided into several branches or the moving
node enters from a narrow alley to a wide square, the accuracy of
the wireless localization based on the surface correlation
according to the present embodiment may be lowered. The relative
localization unit 40 estimates the relative position of the moving
node 1 on the basis of movement sensing of the moving node 1 made
by the sensor unit 20, and thereby, a route change such as a case
where one path is divided into several branches or the moving node
enters from a narrow alley to a wide square does not influence the
accuracy of the estimated value of the relative position of the
moving node 1.
[0127] The present embodiment corrects a coordinate value of a
reception position of the moving node 1 estimated by a relative
localization based on movement sensing of the moving node 1 by
using the coordinate value of the reception position of the moving
node 1 estimated by the wireless localization based on the surface
correlation, and thereby, not only an error of the relative
position of the moving node 1 can be reduced through mutual
correction between defects in the relative localization and defects
in the wireless localization, but also accuracy of the estimated
value of the reception position of the moving node 1 can be
prevented a decrease due to a change in the route or the like. That
is, the present embodiment can accurately estimate a current
position of the moving node 1 all the time by performing mutual
complementing between defects of the relative localization and
defects of the relative localization even in various environmental
changes such as a wireless environment change or a route change,
and a map in which very accurate route information is recorded can
be created by recording the current position of the moving node 1
which is accurately estimated in this way and the strength of the
signal received at the current position.
[0128] In step 310, the position correction unit 70 corrects the
coordinate value of the relative position of the moving node 1
estimated in step 230 by using the coordinate value of the
reception position of the moving node 1 estimated in step 450 in
accordance with following Equation 1, thereby, correcting the
relative position estimated in step 230. Equation 1 is an equation
for calculating X*, that is, new n coordinate values, which
minimizes the sum of left square terms and the sum of right square
terms with respect to n position coordinate values corresponding to
X. Equation 1 can be obtained by using a Gauss-Newton method or the
like.
X * = arg min X i f ( x i , u i + 1 ) - x i + 1 2 + jk g ( x j , v
j ) - x k 2 Equation 1 ##EQU00001##
[0129] In a case where the moving node 1 is a smartphone carried by
a user, X={x1, x2, x3, . . . , x.sub.i, x.sub.i+1, . . . ,
x.sub.n}.sup.T in Equation 1 on assumption that the user walked by
n-1 steps from a position x.sub.1 of the first step. Here, a
superscript T means a column vector in which n position coordinate
values are arranged in a line. Since a movement speed and a
movement direction of the user are greatly changed, each step of
the user is set as each localization point on a movement route of
the moving node 1. The step of the user can be searched by a sensor
embedded in the smartphone. That is, the moving node 1 estimates a
relative position and simultaneously estimates a reception position
at each step of the user. If the moving node 1 is a navigation
system mounted on a vehicle, a localization point may be set for
each distance on the movement route of the moving node 1 instead of
steps of the user.
[0130] "x.sub.i" of the left square term means a coordinate value
of a relative position of an i-th step with respect to an (i-1)th
step of a user, "x.sub.i+1" means a coordinate value of a relative
position of (i+1)th step with respect to the ith step of the user,
and "u.sub.i+1" means a movement distance and a movement direction
to the relative position of (i+1)th step from the relative position
of the ith step. "f(x.sub.i, u.sub.i+1)" means a function for
calculating a coordinate value of the relative position of the
(i+1)th step of the user by applying u.sub.i+1 to the coordinate
value of relative position of the ith step of the user. If a
coordinate system of a map created according to the present
embodiment is a two-dimensional coordinate system, "f(x.sub.i,
u.sub.i+1)" may be represented by following Equation 2
x.sub.i+1,1=x.sub.i,1+cos(h.sub.i+1)l.sub.i+1
x.sub.i+1,2=x.sub.i,2+sin(h.sub.i+1)l.sub.i+1 Equation 2)
[0131] In Equation 2, a coordinate value of "x.sub.i" is
(x.sub.i,1, x.sub.i,2) and a coordinate value of "x.sub.i+1" is
(x.sub.i+1,1, x.sub.i+1,2). "h.sub.i+1" indicates a movement
direction from the relative position of the ith step to the
relative position of the (i+1)th step, and "l.sub.i+1" indicates a
movement distance. "u.sub.i+1" is a movement distance and a
movement direction calculated from a value of the output signal of
the sensor unit 20. There is always an error in the movement
distance and the movement direction calculated from the value of
the output signal of the sensor unit 20, that is, that is,
u.sub.i+1 due to a hardware error of the sensor itself, noise
introduced into the sensor, and the like. Accordingly, there is
always an error in the calculated value of f(x.sub.i, u.sub.i+1),
that is, the estimated value of the relative position of the moving
node 1, and in order to increase accuracy of relative localization,
a constraint that the error has to be minimized occurs. The error
constraint of this relative localization is represented by a left
square term of Equation 1.
[0132] For example, in order to reduce an error of the relative
localization, "x.sub.i+1" can be calculated by applying a current
progress direction of a user and an average step width of the user
to the coordinate value of the relative position of the ith step of
the user. If a left square term is calculated for X={x1, x2, x3, .
. . , x.sub.i, x.sub.i+1, . . . , x.sub.n}.sup.T and X={x1, x2, x3,
. . . , x.sub.i, x.sub.i+1, . . . , x.sub.n}.sup.T is updated by
using a new value that minimizes the calculated value, it is
possible to prevent a phenomenon in which the estimated value of
the relative position of the moving node 1 excessively deviates
from an actual position of the moving node 1 due to an error of a
sensor, from occurring. As described above, an error of a relative
localization algorithm such as PDR or DR has characteristics in
which errors of the relative position of the moving node 1 are
accumulated as estimation of the relative position of the moving
node 1 is repeated. Due to the characteristics and other causes,
there is a limit to increase accuracy of the relative localization
only by using a constraint of minimizing the error of f(x.sub.i,
u.sub.i+1). The present embodiment improves accuracy of the
estimated value of the relative position of the moving node 1 by
adding a constraint that minimizes the right square term to the
constraint that minimizes the left square term.
[0133] "X.sub.j" of a right square term means a current position of
the moving node 1 that receives a signal sent from the fixed node
2, "V.sub.j" means a change pattern of of the signal strength
generated at a current position x.sub.j of the moving node 1, and
"X.sub.k" means an absolute position of a localization point having
a pattern most similar to the change pattern of the signal strength
generated at a current position x.sub.j of the moving node 1 within
a signal strength distribution in a target region among the
localization points of the map stored in the storage 30.
"g(x.sub.j, v.sub.j)" means a function for calculating an absolute
position of the part having a pattern most similar to the change
pattern v.sub.j of the signal strength generated at the current
position x.sub.j of the moving node 1 within the signal strength
distribution in the target region.
[0134] As described above, the comparison unit 65 searches for a
pattern most similar to the change pattern v.sub.j of the signal
strength generated at the position x.sub.j of the moving node 1,
based on the surface correlation between the change pattern v.sub.j
of the signal strength generated at the current position x.sub.j of
the moving node 1 and the signal strength distribution in the
target region, and thus, it can be said that "g(x.sub.j, v.sub.j)"
is a function of the wireless localization algorithm based on the
surface correlation described above. That is, the reception
position estimation unit 66 estimates a reception position of the
moving node 1 with respect to a signal received at the current
position x.sub.j of the moving node 1 in accordance with g(x.sub.j,
v.sub.j). In this way, the calculated value of "g(x.sub.j,
v.sub.j)" is the estimated coordinate value of the reception
position of the moving node 1. It may be said that "x.sub.k" is a
localization point corresponding to the previous reception position
having a coordinate value closest to the coordinate value of the
currently estimated reception position of the moving node 1, that
is, the localization point of the map stored in the storage 30.
[0135] In order to satisfy a constraint that a right square term of
Equation 1 has to be minimized, the coordinate value of "g(x.sub.j,
v.sub.j)" and the coordinate value of "x.sub.k" have to be the
same. The coordinate value of "x.sub.k" is any one of the
coordinate values of the localization points stored in the storage
30, that is, the coordinate values closest to the calculated value
of "g(x.sub.j, v.sub.j)" among X={x1, x2, x3, . . . , x.sub.i,
x.sub.i+1, . . . , x.sub.n}.sup.T, and is an estimated value of the
relative position of the moving node 1 obtained by the relative
localization or a corrected value for the estimated value. Since
the signal strength distribution cannot be known when the moving
node 1 first turns a certain route in a target region, the relative
position of the moving node 1 cannot be corrected, and thereby, a
coordinate value of "x.sub.k" becomes the estimated value of a
relative position of the moving node 1 obtained by only the
relative localization.
[0136] Since accuracy of the estimated value of a position of the
moving node 1 due to the wireless localization based on the surface
correlation according to the present embodiment is very high, the
estimated value of the position of the moving node 1 obtained by
the relative localization is the same as the estimated value of the
position of the moving node 1 obtained by the wireless localization
based on the surface correlation, if there is no error of the
relative localization. However, the coordinate value of "g(x.sub.j,
v.sub.j)" and the coordinate value of "x.sub.k" cannot be the same
as each other due to an error of the relative localization,
typically an error of the PDR. Accordingly, in the present
embodiment, the coordinate value of the localization point of the
map stored in the storage 30 is updated so as to make the
coordinate value of the localization point of the map stored in the
storage 30 approach the coordinate value of "g(x.sub.j, v.sub.j)",
and thereby, accuracy of the coordinate value of the localization
point of the map stored in the storage 30 is improved.
[0137] In this way, in step 310, the position correction unit 70
corrects the coordinate value of the relative position of the
moving node 1 estimated in step 230 and the coordinate values of
the respective localization points in the signal strength
distribution of the map stored in the storage 30 in such a manner
that a difference between the coordinate value of the reception
position of the moving node 1 estimated in step 450 in accordance
with Equation 1 and the coordinate value of the localization point
closest to the coordinate value of the reception position among the
localization points within the signal strength distribution of the
map stored in the storage 30 is minimized. The localization points
within the signal strength distribution of the map stored in the
storage 30 are points at which the relative position of the moving
node 1 is previously measured on the movement route of the moving
node 1. The moving node 1 repeatedly updates the coordinate values
of the respective localization points through Equation 1 while
turning a certain route in the target region, thereby, optimizing a
graph of the route represented by a plurality of localization
points so as to approach an actual route.
[0138] As described above, the present embodiment improves the
accuracy of the estimated value of the relative position of the
moving node 1 by adding a constraint that minimizes the right
square term to a constraint which minimizes the left square term.
That is, in step 310, the position correction unit 70 corrects the
coordinate value of the relative position of the moving node 1
estimated in step 230 and the coordinate values of the respective
localization points in the signal strength distribution of the map
stored in the storage 30, in such a manner that an error between
the coordinate value of the relative position estimated in step 450
in accordance with Equation 1 and a coordinate value of another
relative position of the moving node 1 based on the relative
position is minimized, and at the same time, a difference between
the coordinate value of the reception position of the moving node 1
estimated in step 450 and the coordinate value of the localization
point closest to the coordinate value of the reception position
among the localization points within the signal strength
distribution of the map stored in the storage 30 is minimized.
[0139] In this way, in the present embodiment, the estimated value
of the relative position of the moving node 1 is not corrected by
using the estimated value of the position of the moving node 1
obtained by the wireless localization based on the surface
correlation as it is, and a constraint is added in which the error
between the coordinate value of the relative position estimated in
step 450 and the coordinate value of another relative position of
the moving node 1 based on the relative position has to be
minimized, and thereby, it is possible to accurately estimate a
current position of the moving node 1by performing mutual
complementing between defects in the relative localization and
defects in the wireless localization even in various environment
changes such as a wireless environment change and a route change,
and it is possible to create a map in which very accurate route
information is recorded by recording the coordinate value of the
position of the moving node 1 accurately estimated in this way and
the strength of the signal received at the position.
[0140] FIGS. 13A to 13C are diagrams illustrating an example of
correction of a relative position made by the SLAM according to the
present embodiment. In each of FIGS. 13A to 13C, step numbers of
the moving node 1 are marked at the relative positions of the
moving node 1 estimated by the relative localization unit 50 for
each step using the PDR algorithm, when a user holds a smartphone
in his hand, starts from a first localization point, walks 11
steps, and returns to the first localization point. The moving node
1 estimates a relative position indicating a second localization
point with respect to the first localization point from a movement
distance calculated from a value of an output signal of the sensor
unit 20 and a moving direction while moving by one step after
starting from the first localization point. In the same manner, the
moving node 1 estimates the relative positions of the moving node 1
at third to twelfth localization points while moving by one step
from the second localization point.
[0141] Since the user started from the first localization point and
returned to the first localization point, the first localization
point and the 12th localization point are the same position in an
actual physical terrain. As illustrated in FIG. 13A, although the
12th localization point has to be marked on the 1st localization
point so as to be overlapped, the 12th localization point is marked
at another position around the first localization point due to
accumulation of PDR errors. In the SLAM algorithm of related art, a
loop closure indicates a process of improving accuracy of a map by
adjusting a coordinate value of a landmark estimated at the time of
arrival to a coordinate value of a landmark at the time of start,
in a case where the coordinate value of the landmark estimated at
the time of arrival differs from the coordinate value of the
landmark at the time of start, when a user starts from a certain
landmark, moves along a loop-shaped route, and returns to the same
landmark.
[0142] As illustrated in FIG. 13B, if only the 12th localization
point is moved to the 1st localization point, an error of the
relative localization at the other localization points is
neglected. Although the moving node 1 turned in a root form, a
starting point and an ending point are a main cause of accumulation
of the PDR errors, that is, accumulation of relative localization
errors at all the localization points, and thus, the relative
localization errors at each localization point has to be corrected
as illustrated in FIG. 13C. As described above, the SLAM algorithm
of related art has a problem of the loop closure through
identification of the landmark, and in order to identify the
landmark, a sensor such as a LiDAR, a camera, or an ultrasonic
sensor is required.
[0143] In a case where the moving node 1 starts from the starting
point corresponding to the first localization point, turns a route
of a route form, and arrives at a destination corresponding to a
geographically identical position to the starting point, in step
310, the position correction unit 70 corrects the coordinate value
of the relative position of the moving node 1 estimated in step 230
and the coordinate values of each localization point in the signal
strength distribution of the map stored in the storage 30, such
that a difference between a coordinate value of an arrival point
which is the reception position of the moving node 1 estimated in
step 450 according to Equation 1 and a coordinate value of the
starting point which is a localization point closest to the
coordinate value of the arrival point among the localization points
in the signal strength distribution of the map stored in the
storage 30. Here, the coordinate value of the arrival point which
is the reception position of the moving node 1 estimated in step
450 is the coordinate value of the position of the moving node 1
estimated by the wireless localization based on the surface
correction, and thus, accuracy of the position is very high.
[0144] Accordingly, an error of the estimated value of the relative
position of the arrival point can be greatly reduced through the
process of adjusting the coordinate value of the arrival point so
as to match or approximate the coordinate value of the starting
point which is the reception position of the moving node 1
estimated in step 450, without identifying the landmark using a
sensor such as a LiDAR, a camera, or an ultrasonic sensor. In the
present embodiment, similarity between the change pattern of the
signal strength generated by the pattern generation unit 64 and a
corresponding pattern in the signal strength distribution of the
map stored in the storage 30 plays a role of a kind of landmark
instead of a physical landmark. According to the present
embodiment, a problem of the loop closure is solved even without a
separate sensor for a physical identification of the landmark, and
thereby, accuracy of a map can be improved.
[0145] FIGS. 14A to 15F are diagrams obtained by comparing SLAM
performance tests for the wireless localization algorithm or
related art and the wireless localization algorithm according to
the present embodiment with each other. While a user starts from a
starting point (0, 0) and turns around a corridor of a building
with a smartphone in his hand in the target region illustrated in
FIG. 8, the user estimates the reception position of the moving
node 1 for each localization point on the movement route of the
moving node 1 using the wireless localization algorithm, and while
the coordinate value of the relative position of the moving node 1
is corrected by using the estimated value of the reception position
of the moving node 1, a dot is marked in each position
corresponding to the corrected coordinate value in a coordinate
system of the map stored in the storage 30, and dots are connected
to each other. FIG. 14A illustrates a movement trajectory of the
moving node 1 corresponding to arrangement of a plurality of
localization point dots marked through this process.
[0146] FIG. 14B illustrates the movement trajectory of the moving
node 1 in the same manner as in the SLAM performance test of FIG.
14A, except that the reception position of the moving node 1 is
estimated for each localization point using the wireless
localization algorithm based on the surface correlation of the
present embodiment instead of the wireless localization algorithm
of related art. Particularly, the tests of FIGS. 14A and 14B were
performed in a state where all the dozens of access points
installed in the target region illustrated in FIG. 8 operated. It
can be seen that the movement trajectory of the moving node 1
estimated by the SLAM algorithm employing the wireless localization
algorithm based on the surface correlation of the present
embodiment is much closer to an actual shape of a corridor of a
building than the movement trajectory of the moving node 1
estimated by the SLAM algorithm employing the wireless localization
algorithm of related art.
[0147] FIGS. 15A to 15C illustrate the movement trajectories of the
moving node 1 in a case where a test is performed in the same
manner as the test of FIG. 14A in a state where the access points
installed in the target region illustrated in FIG. 8 operated at
50%, 20%, and 10%, respectively. FIGS. 15D to 15F illustrate the
movement trajectories of the moving node 1 in a case where a test
is performed in the same manner as the test of FIG. 14B in a state
where the access points installed in the target region illustrated
in FIG. 8 operated at 50%, 20%, and 10%, respectively. It can be
seen from FIGS. 15A to 15C that the movement trajectory of the
moving node 1 estimated by the SLAM algorithm employing the
wireless localization algorithm of related art is very sensitive to
the number of access points thereby being greatly distorted.
[0148] It can be seen from FIGS. 15D to 15F that the movement
trajectory of the moving node 1 estimated by the SLAM algorithm
employing the wireless localization algorithm based on the surface
correlation of the present embodiment maintains a shape similar to
an actual shape of a corridor of a building without being greatly
influenced by the number of access points installed in the target
region illustrated in FIG. 8. In this way, in the present
embodiment, the smaller number of access points than the number of
access points required for establishing a good wireless environment
are installed in a certain region, and thereby, a map in which very
accurate route information is recorded can be created even in a
case where a wireless environment poor.
[0149] As described above, in the wireless localization algorithm
of related art, an error of the estimated value of the reception
position of the moving node 1 is very large, in a case where there
is a wireless environment change such as signal interference
between communication channels, extension of an access point, and
occurrence of a failure or an obstacle, or wireless environment is
poor due to lack of the number of access points. On the other hand,
the SLAM algorithm according to the present embodiment estimates
the reception position of the moving node 1 using a change pattern
of at least one signal strength according to a relative change of a
position of the moving node over a plurality of time points, and
thus an error of the estimated value rarely occurs even if there is
a wireless environment change such as signal interference between
communication channels, extension of an access point, and
occurrence of a failure or an obstacle, or wireless environment is
poor due to lack of the number of access points. As a result, the
SLAM algorithm employing the wireless localization algorithm based
on the surface correlation of the present embodiment can create a
map in which very accurate route information is recorded, even in a
case where there is a wireless environment change such as a change
in the number of access points, or wireless environment is poor due
to lack of the number of access points.
[0150] In step 320, the mapping unit 80 of the wireless
localization unit 60 of the moving node 1 represents the route of
the region using the relative position of the moving node 1
corrected in step 310, thereby, creating a map for the target
region. Whenever the steps 110, 120, 130, and 310 are repeatedly
performed for each of the plurality of time points, the map
creation unit 80 arranges and records a plurality of coordinate
values of the relative positions corrected at a plurality of time
points in storage 30 in step 320, thereby, representing the route
of the target region, and maps the coordinate values of the
relative positions of the moving node 1 corrected at each time
point in the storage 30 and records an ID of at least one fixed
node transmitting at least one signal received at the same time
point as each time point and strength of the at least one signal,
thereby, creating a map of the signal strength distribution of the
target region. In this way, since the position estimation of the
moving node 1 and the map creation can be performed at the same
time, time and cost consumed in the map creation can be greatly
reduced compared with a method in which a person directly collects
terrain information or signal information.
[0151] If the moving node 1 repeatedly travels all the routes in
the target region by the number of times corresponding to a target
map accuracy of the present embodiment, a map in which round
information, signal strength information, and fixed node
information with very high accuracy are recorded is completed and
stored in the storage 30. In a case where a map created by the SLAM
algorithm according to the present embodiment includes a radio map
based on a Wi-Fi signal, as illustrated in FIG. 7, route
information, Wi-Fi signal strength information, and access point
information are recorded in the map, and ln a case where a map
created by the SLAM algorithm according to the present embodiment
includes a radio map based on an LTE signal, route information, LTE
signal strength information, and base station information are
recorded in the map. If a map with a target accuracy is stored in
the storage 30, the map creation unit 80 transmits the map created
in this way to the localization server 3 through the wireless
communication unit 10.
[0152] FIGS. 16A and 16B are diagrams illustrating examples of maps
generated by the SLAM algorithm according to the present
embodiment. FIG. 16A illustrates a map created by the SLAM
algorithm according to the present embodiment with respect to the
target region illustrated in FIG. 8. It can be seen from FIG. 16A
that a corridor shape in the map created by the SLAM algorithm
according to the present embodiment is represented to be
approximate an actual corridor shape illustrated in FIG. 8 by
arrangement of the localization points having the coordinate values
of the relative positions of the moving node 1 corrected by the
SLAM algorithm according to the present embodiment. A route graph
in the map can be generated from the numerical values in the table
illustrated in FIG. 7. FIG. 16B illustrates an example of the map
created by the SLAM algorithm according to the present embodiment
with respect to a target region having various routes. The map
illustrated in FIG. 16B can be created in the same manner as in the
example of FIG. 16A by the SLAM algorithm described above.
[0153] As illustrated in FIGS. 16A and 16B, a physical terrain of
the real world is simplified into a plurality of map links and a
plurality of map nodes to be represented in the map created by the
SLAM algorithm according to the present embodiment. Each map link
represents a linear path through which a person or a vehicle can
pass, and each map node is represented in the form of a map node
indicating a point where a plurality of map links joins or a point
where one map link curves. That is, each map node indicates a point
where a path crosses a path, a point where one path starts to
divide into several branches, or a point where a road curves. In
the present embodiment, each map link becomes one cluster. A
sequence number of each cluster is represented in each map link.
Any moving node can perform the wireless localization based on the
surface correlation described above or perform the wireless
localization of related art by receiving data of at least one
cluster around clusters of the map stored in the localization
server 3. Particularly, in a case where the wireless localization
based on the surface correlation is performed on the map created by
the SLAM algorithm according to the present embodiment, it is
possible to obtain results of localization with very high
accuracy.
[0154] Meanwhile, the integrated localization method according to
the embodiment of the present invention described above can be
implemented by a program executable in a processor of a computer
and can be implemented by a computer that records the program in a
computer-readable recording medium and execute the program. The
computer includes any type of computer capable of executing a
program, such as a desktop computer, a notebook computer, a
smartphone, an embedded type computer, and the like. In addition, a
structure of data used for the above-described embodiment according
to the present invention can be recorded in a computer-readable
recording medium through various means. The computer readable
recording medium includes a storage medium such as a RAM, a ROM, a
magnetic storage medium (for example, a floppy disk, a hard disk,
and the like), and an optically readable medium (for example, a CD
ROM, a DVD, and the like).
[0155] The present invention is described above with reference to
preferred embodiments thereof. It will be understood by those
skilled in the art that the present invention may be embodied in
various forms without departing from the spirit or essential
characteristics thereof. Therefore, the disclosed embodiments
should be considered in an illustrative viewpoint rather than a
restrictive viewpoint. The scope of the present invention is
defined by the appended claims rather than by the above
description, and all differences within the scope of equivalents
thereof should be construed as being included in the present
invention.
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